rna-seq datasets Search Results


rna-seq dataset  (Novogene)
90
Novogene rna-seq dataset
Rna Seq Dataset, supplied by Novogene, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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hpa analysis of tcga rna-seq data  (Human Protein Atlas)
90
Human Protein Atlas hpa analysis of tcga rna-seq data
Hpa Analysis Of Tcga Rna Seq Data, supplied by Human Protein Atlas, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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rna-seq  (Dawley Inc)
90
Dawley Inc rna-seq
Rna Seq, supplied by Dawley Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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rna-seq datasets  (Epigenomics ag)
90
Epigenomics ag rna-seq datasets
(A) Composition of the four datasets extracted from FANTOM5 : two are composed of biologically-similar cell populations and two are composed of dissimilar cell populations. (B) Number of relations inferred by Regulus for each of the datasets depicted in A. (C-D) Distribution of the number of TFs regulating an expressed gene in Regulus circuits before (C) and after (D) the filtering using likelihood constraints. (E) Overlap of the relations found in the regulatory circuits corresponding to the four datasets of A. (F) Percentage of genes from <t>Roadmap</t> Epigenomics RNA-seq datasets related to the cell populations found in Regulus inferred circuits. Genes are separated in three categories according to expression levels: the top 10%, the middle 10% and the bottom 10%. (G) For each dataset, number of relations which were present in each of the five database (“reachable”), number of these relations which were inferred by Regulus (“found”) and p-values of enrichment for existing relations in Regulus inferred circuits, as assessed by binomial test. (H) Relations found in Trrust and Signor and coherence of signs. True : percentage of relations found with the same sign as in the database, Unknown : relations non signed or signed + and—in the databases. For the intersection of Trrust and Signor: Different : number of relations signed differently in the two databases, True : percentage of relations having the same sign after inference by Regulus as in both databases.
Rna Seq Datasets, supplied by Epigenomics ag, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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tcga gbm rna-seq dataset  (CH Instruments)
90
CH Instruments tcga gbm rna-seq dataset
The development of secretory pathway kinase or kinase-like proteins (SPKKPs) gene signature stratifies the IDH wild type (wt) <t>GBM</t> as two groups with distinct survival. ( A ) The coefficient profiles of 13 SPKKPs genes with the gradual increase of lambda by LASSO regression <t>(TCGA</t> GBM RNA-seq, IDH wt, n = 142). ( B ) LASSO regression analysis with cross-validation method identified a SPKKPs gene signature including 3 members in this family ( FAM20A, FAM20A , and C3orf58 ) with prognostic value in IDH wt GBM <t>(TCGA,</t> n = 142). ( C ) Heatmap showing the association of 13 SPKKPs gene expression with clinicopathologic features in low- and high-risk GBM groups defined by the secretory pathway kinase related gene signature (TCGA GBM RNA-seq: low risk: n = 71, high risk: n = 71, Chi-square test). ( D ) Kaplan–Meier curves describing the survival of IDH wt GBM in low- and high-risk groups defined by secretory pathway-related gene signature (TCGA GBM RNA-seq: low risk: n = 71, high risk: n = 71, Log rank test, P = 0.0312). ( E ) The expression of SPKKPs member genes in different WHO grades of glioma (TCGA RNA-seq: grade II: n = 260, grade III: n = 267, GBM: n = 168, one-way ANOVA). ( F ) The expression of SPKKPs member genes in GBM with different IDH status (TCGA GBM RNA-seq: IDH mutant (mut): n = 11, IDH wt: n = 144, t -test). ( G ) The expression of SPKKPs member genes in all low-grade gliomas (LGG) and IDH -mut LGG with different 1p/19q codeletion (codel) status (TCGA RNA-seq: LGG 1p19q codel: n = 160, LGG 1p19q non-codel: n = 317; LGG with IDH mut 1p19q codel: n = 160, LGG with IDH mut 1p19q non-codel: n = 230, t -test). (ns P > 0.05, * P < 0.05, *** P < 0.001, and **** P < 0.0001).
Tcga Gbm Rna Seq Dataset, supplied by CH Instruments, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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whole exome sequencing and rna-seq datasets  (Omics Data Automation)
90
Omics Data Automation whole exome sequencing and rna-seq datasets
The development of secretory pathway kinase or kinase-like proteins (SPKKPs) gene signature stratifies the IDH wild type (wt) <t>GBM</t> as two groups with distinct survival. ( A ) The coefficient profiles of 13 SPKKPs genes with the gradual increase of lambda by LASSO regression <t>(TCGA</t> GBM RNA-seq, IDH wt, n = 142). ( B ) LASSO regression analysis with cross-validation method identified a SPKKPs gene signature including 3 members in this family ( FAM20A, FAM20A , and C3orf58 ) with prognostic value in IDH wt GBM <t>(TCGA,</t> n = 142). ( C ) Heatmap showing the association of 13 SPKKPs gene expression with clinicopathologic features in low- and high-risk GBM groups defined by the secretory pathway kinase related gene signature (TCGA GBM RNA-seq: low risk: n = 71, high risk: n = 71, Chi-square test). ( D ) Kaplan–Meier curves describing the survival of IDH wt GBM in low- and high-risk groups defined by secretory pathway-related gene signature (TCGA GBM RNA-seq: low risk: n = 71, high risk: n = 71, Log rank test, P = 0.0312). ( E ) The expression of SPKKPs member genes in different WHO grades of glioma (TCGA RNA-seq: grade II: n = 260, grade III: n = 267, GBM: n = 168, one-way ANOVA). ( F ) The expression of SPKKPs member genes in GBM with different IDH status (TCGA GBM RNA-seq: IDH mutant (mut): n = 11, IDH wt: n = 144, t -test). ( G ) The expression of SPKKPs member genes in all low-grade gliomas (LGG) and IDH -mut LGG with different 1p/19q codeletion (codel) status (TCGA RNA-seq: LGG 1p19q codel: n = 160, LGG 1p19q non-codel: n = 317; LGG with IDH mut 1p19q codel: n = 160, LGG with IDH mut 1p19q non-codel: n = 230, t -test). (ns P > 0.05, * P < 0.05, *** P < 0.001, and **** P < 0.0001).
Whole Exome Sequencing And Rna Seq Datasets, supplied by Omics Data Automation, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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rna-seq data navigator  (Allen Institute for Brain Science)
90
Allen Institute for Brain Science rna-seq data navigator
The development of secretory pathway kinase or kinase-like proteins (SPKKPs) gene signature stratifies the IDH wild type (wt) <t>GBM</t> as two groups with distinct survival. ( A ) The coefficient profiles of 13 SPKKPs genes with the gradual increase of lambda by LASSO regression <t>(TCGA</t> GBM RNA-seq, IDH wt, n = 142). ( B ) LASSO regression analysis with cross-validation method identified a SPKKPs gene signature including 3 members in this family ( FAM20A, FAM20A , and C3orf58 ) with prognostic value in IDH wt GBM <t>(TCGA,</t> n = 142). ( C ) Heatmap showing the association of 13 SPKKPs gene expression with clinicopathologic features in low- and high-risk GBM groups defined by the secretory pathway kinase related gene signature (TCGA GBM RNA-seq: low risk: n = 71, high risk: n = 71, Chi-square test). ( D ) Kaplan–Meier curves describing the survival of IDH wt GBM in low- and high-risk groups defined by secretory pathway-related gene signature (TCGA GBM RNA-seq: low risk: n = 71, high risk: n = 71, Log rank test, P = 0.0312). ( E ) The expression of SPKKPs member genes in different WHO grades of glioma (TCGA RNA-seq: grade II: n = 260, grade III: n = 267, GBM: n = 168, one-way ANOVA). ( F ) The expression of SPKKPs member genes in GBM with different IDH status (TCGA GBM RNA-seq: IDH mutant (mut): n = 11, IDH wt: n = 144, t -test). ( G ) The expression of SPKKPs member genes in all low-grade gliomas (LGG) and IDH -mut LGG with different 1p/19q codeletion (codel) status (TCGA RNA-seq: LGG 1p19q codel: n = 160, LGG 1p19q non-codel: n = 317; LGG with IDH mut 1p19q codel: n = 160, LGG with IDH mut 1p19q non-codel: n = 230, t -test). (ns P > 0.05, * P < 0.05, *** P < 0.001, and **** P < 0.0001).
Rna Seq Data Navigator, supplied by Allen Institute for Brain Science, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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bulk rna-seq profiles  (Muris Inc)
90
Muris Inc bulk rna-seq profiles
The development of secretory pathway kinase or kinase-like proteins (SPKKPs) gene signature stratifies the IDH wild type (wt) <t>GBM</t> as two groups with distinct survival. ( A ) The coefficient profiles of 13 SPKKPs genes with the gradual increase of lambda by LASSO regression <t>(TCGA</t> GBM RNA-seq, IDH wt, n = 142). ( B ) LASSO regression analysis with cross-validation method identified a SPKKPs gene signature including 3 members in this family ( FAM20A, FAM20A , and C3orf58 ) with prognostic value in IDH wt GBM <t>(TCGA,</t> n = 142). ( C ) Heatmap showing the association of 13 SPKKPs gene expression with clinicopathologic features in low- and high-risk GBM groups defined by the secretory pathway kinase related gene signature (TCGA GBM RNA-seq: low risk: n = 71, high risk: n = 71, Chi-square test). ( D ) Kaplan–Meier curves describing the survival of IDH wt GBM in low- and high-risk groups defined by secretory pathway-related gene signature (TCGA GBM RNA-seq: low risk: n = 71, high risk: n = 71, Log rank test, P = 0.0312). ( E ) The expression of SPKKPs member genes in different WHO grades of glioma (TCGA RNA-seq: grade II: n = 260, grade III: n = 267, GBM: n = 168, one-way ANOVA). ( F ) The expression of SPKKPs member genes in GBM with different IDH status (TCGA GBM RNA-seq: IDH mutant (mut): n = 11, IDH wt: n = 144, t -test). ( G ) The expression of SPKKPs member genes in all low-grade gliomas (LGG) and IDH -mut LGG with different 1p/19q codeletion (codel) status (TCGA RNA-seq: LGG 1p19q codel: n = 160, LGG 1p19q non-codel: n = 317; LGG with IDH mut 1p19q codel: n = 160, LGG with IDH mut 1p19q non-codel: n = 230, t -test). (ns P > 0.05, * P < 0.05, *** P < 0.001, and **** P < 0.0001).
Bulk Rna Seq Profiles, supplied by Muris Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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tissue-specific rna-seq data  (Human Protein Atlas)
90
Human Protein Atlas tissue-specific rna-seq data
The development of secretory pathway kinase or kinase-like proteins (SPKKPs) gene signature stratifies the IDH wild type (wt) <t>GBM</t> as two groups with distinct survival. ( A ) The coefficient profiles of 13 SPKKPs genes with the gradual increase of lambda by LASSO regression <t>(TCGA</t> GBM RNA-seq, IDH wt, n = 142). ( B ) LASSO regression analysis with cross-validation method identified a SPKKPs gene signature including 3 members in this family ( FAM20A, FAM20A , and C3orf58 ) with prognostic value in IDH wt GBM <t>(TCGA,</t> n = 142). ( C ) Heatmap showing the association of 13 SPKKPs gene expression with clinicopathologic features in low- and high-risk GBM groups defined by the secretory pathway kinase related gene signature (TCGA GBM RNA-seq: low risk: n = 71, high risk: n = 71, Chi-square test). ( D ) Kaplan–Meier curves describing the survival of IDH wt GBM in low- and high-risk groups defined by secretory pathway-related gene signature (TCGA GBM RNA-seq: low risk: n = 71, high risk: n = 71, Log rank test, P = 0.0312). ( E ) The expression of SPKKPs member genes in different WHO grades of glioma (TCGA RNA-seq: grade II: n = 260, grade III: n = 267, GBM: n = 168, one-way ANOVA). ( F ) The expression of SPKKPs member genes in GBM with different IDH status (TCGA GBM RNA-seq: IDH mutant (mut): n = 11, IDH wt: n = 144, t -test). ( G ) The expression of SPKKPs member genes in all low-grade gliomas (LGG) and IDH -mut LGG with different 1p/19q codeletion (codel) status (TCGA RNA-seq: LGG 1p19q codel: n = 160, LGG 1p19q non-codel: n = 317; LGG with IDH mut 1p19q codel: n = 160, LGG with IDH mut 1p19q non-codel: n = 230, t -test). (ns P > 0.05, * P < 0.05, *** P < 0.001, and **** P < 0.0001).
Tissue Specific Rna Seq Data, supplied by Human Protein Atlas, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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rna-seq datasets  (Novogene)
90
Novogene rna-seq datasets
The development of secretory pathway kinase or kinase-like proteins (SPKKPs) gene signature stratifies the IDH wild type (wt) <t>GBM</t> as two groups with distinct survival. ( A ) The coefficient profiles of 13 SPKKPs genes with the gradual increase of lambda by LASSO regression <t>(TCGA</t> GBM RNA-seq, IDH wt, n = 142). ( B ) LASSO regression analysis with cross-validation method identified a SPKKPs gene signature including 3 members in this family ( FAM20A, FAM20A , and C3orf58 ) with prognostic value in IDH wt GBM <t>(TCGA,</t> n = 142). ( C ) Heatmap showing the association of 13 SPKKPs gene expression with clinicopathologic features in low- and high-risk GBM groups defined by the secretory pathway kinase related gene signature (TCGA GBM RNA-seq: low risk: n = 71, high risk: n = 71, Chi-square test). ( D ) Kaplan–Meier curves describing the survival of IDH wt GBM in low- and high-risk groups defined by secretory pathway-related gene signature (TCGA GBM RNA-seq: low risk: n = 71, high risk: n = 71, Log rank test, P = 0.0312). ( E ) The expression of SPKKPs member genes in different WHO grades of glioma (TCGA RNA-seq: grade II: n = 260, grade III: n = 267, GBM: n = 168, one-way ANOVA). ( F ) The expression of SPKKPs member genes in GBM with different IDH status (TCGA GBM RNA-seq: IDH mutant (mut): n = 11, IDH wt: n = 144, t -test). ( G ) The expression of SPKKPs member genes in all low-grade gliomas (LGG) and IDH -mut LGG with different 1p/19q codeletion (codel) status (TCGA RNA-seq: LGG 1p19q codel: n = 160, LGG 1p19q non-codel: n = 317; LGG with IDH mut 1p19q codel: n = 160, LGG with IDH mut 1p19q non-codel: n = 230, t -test). (ns P > 0.05, * P < 0.05, *** P < 0.001, and **** P < 0.0001).
Rna Seq Datasets, supplied by Novogene, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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online single-cell rna-seq in adipose tissue dataset  (Broad Institute Inc)
90
Broad Institute Inc online single-cell rna-seq in adipose tissue dataset
The development of secretory pathway kinase or kinase-like proteins (SPKKPs) gene signature stratifies the IDH wild type (wt) <t>GBM</t> as two groups with distinct survival. ( A ) The coefficient profiles of 13 SPKKPs genes with the gradual increase of lambda by LASSO regression <t>(TCGA</t> GBM RNA-seq, IDH wt, n = 142). ( B ) LASSO regression analysis with cross-validation method identified a SPKKPs gene signature including 3 members in this family ( FAM20A, FAM20A , and C3orf58 ) with prognostic value in IDH wt GBM <t>(TCGA,</t> n = 142). ( C ) Heatmap showing the association of 13 SPKKPs gene expression with clinicopathologic features in low- and high-risk GBM groups defined by the secretory pathway kinase related gene signature (TCGA GBM RNA-seq: low risk: n = 71, high risk: n = 71, Chi-square test). ( D ) Kaplan–Meier curves describing the survival of IDH wt GBM in low- and high-risk groups defined by secretory pathway-related gene signature (TCGA GBM RNA-seq: low risk: n = 71, high risk: n = 71, Log rank test, P = 0.0312). ( E ) The expression of SPKKPs member genes in different WHO grades of glioma (TCGA RNA-seq: grade II: n = 260, grade III: n = 267, GBM: n = 168, one-way ANOVA). ( F ) The expression of SPKKPs member genes in GBM with different IDH status (TCGA GBM RNA-seq: IDH mutant (mut): n = 11, IDH wt: n = 144, t -test). ( G ) The expression of SPKKPs member genes in all low-grade gliomas (LGG) and IDH -mut LGG with different 1p/19q codeletion (codel) status (TCGA RNA-seq: LGG 1p19q codel: n = 160, LGG 1p19q non-codel: n = 317; LGG with IDH mut 1p19q codel: n = 160, LGG with IDH mut 1p19q non-codel: n = 230, t -test). (ns P > 0.05, * P < 0.05, *** P < 0.001, and **** P < 0.0001).
Online Single Cell Rna Seq In Adipose Tissue Dataset, supplied by Broad Institute Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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rnaseq dataset  (Mendeley Ltd)
90
Mendeley Ltd rnaseq dataset
The development of secretory pathway kinase or kinase-like proteins (SPKKPs) gene signature stratifies the IDH wild type (wt) <t>GBM</t> as two groups with distinct survival. ( A ) The coefficient profiles of 13 SPKKPs genes with the gradual increase of lambda by LASSO regression <t>(TCGA</t> GBM RNA-seq, IDH wt, n = 142). ( B ) LASSO regression analysis with cross-validation method identified a SPKKPs gene signature including 3 members in this family ( FAM20A, FAM20A , and C3orf58 ) with prognostic value in IDH wt GBM <t>(TCGA,</t> n = 142). ( C ) Heatmap showing the association of 13 SPKKPs gene expression with clinicopathologic features in low- and high-risk GBM groups defined by the secretory pathway kinase related gene signature (TCGA GBM RNA-seq: low risk: n = 71, high risk: n = 71, Chi-square test). ( D ) Kaplan–Meier curves describing the survival of IDH wt GBM in low- and high-risk groups defined by secretory pathway-related gene signature (TCGA GBM RNA-seq: low risk: n = 71, high risk: n = 71, Log rank test, P = 0.0312). ( E ) The expression of SPKKPs member genes in different WHO grades of glioma (TCGA RNA-seq: grade II: n = 260, grade III: n = 267, GBM: n = 168, one-way ANOVA). ( F ) The expression of SPKKPs member genes in GBM with different IDH status (TCGA GBM RNA-seq: IDH mutant (mut): n = 11, IDH wt: n = 144, t -test). ( G ) The expression of SPKKPs member genes in all low-grade gliomas (LGG) and IDH -mut LGG with different 1p/19q codeletion (codel) status (TCGA RNA-seq: LGG 1p19q codel: n = 160, LGG 1p19q non-codel: n = 317; LGG with IDH mut 1p19q codel: n = 160, LGG with IDH mut 1p19q non-codel: n = 230, t -test). (ns P > 0.05, * P < 0.05, *** P < 0.001, and **** P < 0.0001).
Rnaseq Dataset, supplied by Mendeley Ltd, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Image Search Results


(A) Composition of the four datasets extracted from FANTOM5 : two are composed of biologically-similar cell populations and two are composed of dissimilar cell populations. (B) Number of relations inferred by Regulus for each of the datasets depicted in A. (C-D) Distribution of the number of TFs regulating an expressed gene in Regulus circuits before (C) and after (D) the filtering using likelihood constraints. (E) Overlap of the relations found in the regulatory circuits corresponding to the four datasets of A. (F) Percentage of genes from Roadmap Epigenomics RNA-seq datasets related to the cell populations found in Regulus inferred circuits. Genes are separated in three categories according to expression levels: the top 10%, the middle 10% and the bottom 10%. (G) For each dataset, number of relations which were present in each of the five database (“reachable”), number of these relations which were inferred by Regulus (“found”) and p-values of enrichment for existing relations in Regulus inferred circuits, as assessed by binomial test. (H) Relations found in Trrust and Signor and coherence of signs. True : percentage of relations found with the same sign as in the database, Unknown : relations non signed or signed + and—in the databases. For the intersection of Trrust and Signor: Different : number of relations signed differently in the two databases, True : percentage of relations having the same sign after inference by Regulus as in both databases.

Journal: PLOS Computational Biology

Article Title: Regulus infers signed regulatory relations from few samples’ information using discretization and likelihood constraints

doi: 10.1371/journal.pcbi.1011816

Figure Lengend Snippet: (A) Composition of the four datasets extracted from FANTOM5 : two are composed of biologically-similar cell populations and two are composed of dissimilar cell populations. (B) Number of relations inferred by Regulus for each of the datasets depicted in A. (C-D) Distribution of the number of TFs regulating an expressed gene in Regulus circuits before (C) and after (D) the filtering using likelihood constraints. (E) Overlap of the relations found in the regulatory circuits corresponding to the four datasets of A. (F) Percentage of genes from Roadmap Epigenomics RNA-seq datasets related to the cell populations found in Regulus inferred circuits. Genes are separated in three categories according to expression levels: the top 10%, the middle 10% and the bottom 10%. (G) For each dataset, number of relations which were present in each of the five database (“reachable”), number of these relations which were inferred by Regulus (“found”) and p-values of enrichment for existing relations in Regulus inferred circuits, as assessed by binomial test. (H) Relations found in Trrust and Signor and coherence of signs. True : percentage of relations found with the same sign as in the database, Unknown : relations non signed or signed + and—in the databases. For the intersection of Trrust and Signor: Different : number of relations signed differently in the two databases, True : percentage of relations having the same sign after inference by Regulus as in both databases.

Article Snippet: Gene inclusion in our circuits is validated with Roadmap Epigenomics RNA-seq datasets used in [ ].

Techniques: RNA Sequencing, Expressing

The development of secretory pathway kinase or kinase-like proteins (SPKKPs) gene signature stratifies the IDH wild type (wt) GBM as two groups with distinct survival. ( A ) The coefficient profiles of 13 SPKKPs genes with the gradual increase of lambda by LASSO regression (TCGA GBM RNA-seq, IDH wt, n = 142). ( B ) LASSO regression analysis with cross-validation method identified a SPKKPs gene signature including 3 members in this family ( FAM20A, FAM20A , and C3orf58 ) with prognostic value in IDH wt GBM (TCGA, n = 142). ( C ) Heatmap showing the association of 13 SPKKPs gene expression with clinicopathologic features in low- and high-risk GBM groups defined by the secretory pathway kinase related gene signature (TCGA GBM RNA-seq: low risk: n = 71, high risk: n = 71, Chi-square test). ( D ) Kaplan–Meier curves describing the survival of IDH wt GBM in low- and high-risk groups defined by secretory pathway-related gene signature (TCGA GBM RNA-seq: low risk: n = 71, high risk: n = 71, Log rank test, P = 0.0312). ( E ) The expression of SPKKPs member genes in different WHO grades of glioma (TCGA RNA-seq: grade II: n = 260, grade III: n = 267, GBM: n = 168, one-way ANOVA). ( F ) The expression of SPKKPs member genes in GBM with different IDH status (TCGA GBM RNA-seq: IDH mutant (mut): n = 11, IDH wt: n = 144, t -test). ( G ) The expression of SPKKPs member genes in all low-grade gliomas (LGG) and IDH -mut LGG with different 1p/19q codeletion (codel) status (TCGA RNA-seq: LGG 1p19q codel: n = 160, LGG 1p19q non-codel: n = 317; LGG with IDH mut 1p19q codel: n = 160, LGG with IDH mut 1p19q non-codel: n = 230, t -test). (ns P > 0.05, * P < 0.05, *** P < 0.001, and **** P < 0.0001).

Journal: OncoTargets and therapy

Article Title: Secretory Pathway Kinase FAM20C , a Marker for Glioma Invasion and Malignancy, Predicts Poor Prognosis of Glioma

doi: 10.2147/OTT.S275452

Figure Lengend Snippet: The development of secretory pathway kinase or kinase-like proteins (SPKKPs) gene signature stratifies the IDH wild type (wt) GBM as two groups with distinct survival. ( A ) The coefficient profiles of 13 SPKKPs genes with the gradual increase of lambda by LASSO regression (TCGA GBM RNA-seq, IDH wt, n = 142). ( B ) LASSO regression analysis with cross-validation method identified a SPKKPs gene signature including 3 members in this family ( FAM20A, FAM20A , and C3orf58 ) with prognostic value in IDH wt GBM (TCGA, n = 142). ( C ) Heatmap showing the association of 13 SPKKPs gene expression with clinicopathologic features in low- and high-risk GBM groups defined by the secretory pathway kinase related gene signature (TCGA GBM RNA-seq: low risk: n = 71, high risk: n = 71, Chi-square test). ( D ) Kaplan–Meier curves describing the survival of IDH wt GBM in low- and high-risk groups defined by secretory pathway-related gene signature (TCGA GBM RNA-seq: low risk: n = 71, high risk: n = 71, Log rank test, P = 0.0312). ( E ) The expression of SPKKPs member genes in different WHO grades of glioma (TCGA RNA-seq: grade II: n = 260, grade III: n = 267, GBM: n = 168, one-way ANOVA). ( F ) The expression of SPKKPs member genes in GBM with different IDH status (TCGA GBM RNA-seq: IDH mutant (mut): n = 11, IDH wt: n = 144, t -test). ( G ) The expression of SPKKPs member genes in all low-grade gliomas (LGG) and IDH -mut LGG with different 1p/19q codeletion (codel) status (TCGA RNA-seq: LGG 1p19q codel: n = 160, LGG 1p19q non-codel: n = 317; LGG with IDH mut 1p19q codel: n = 160, LGG with IDH mut 1p19q non-codel: n = 230, t -test). (ns P > 0.05, * P < 0.05, *** P < 0.001, and **** P < 0.0001).

Article Snippet: Figure 4 FAM20C knockdown suppresses the migration, invasion, and colony formation of GBM cells. ( A ) GSEA with TCGA GBM RNA-seq dataset disclosed a significant enrichment of cell adhesion- and immune response-related phenotypes in GBM patients with high FAM20C expression. ( B ) Heat maps describing the association between FAM20C expression and cell adhesion and negative immune-regulation‐related genes in TCGA GBM RNA-seq dataset (Chi-square test). ( C ) KEGG analysis was performed on genes with a correlation coefficient greater than 0.3 (Spearman analysis) with FAM20C in TCGA GBM RNA-seq dataset. ( D ) qPCR analyses of FAM20C, MMP2, and MMP9 mRNA expression in LN229 cells transfected with siRNA targeting FAM20C or a non-targeting control (n = 4, t -test). ( E ) Transwell assays demonstrating FAM20C knock-down inhibited the migration (upper panel) and invasion (lower panel) capabilities of LN229 cells (n = 10, t -test). ( ) The colony formation assay showing FAM20C knock-down significantly inhibited the colony formation capability of LN229 cells. (n = 6, t -test). (* P < 0.05, *** P < 0.001, and **** P < 0.0001).

Techniques: RNA Sequencing, Biomarker Discovery, Gene Expression, Expressing, Mutagenesis

Integrative transcriptomic analyses identify FAM20C as the core member of secretory pathway kinase or kinase-like proteins (SPKKPs) family in glioma. ( A ) The protein interaction analysis among SPKKPs member genes with STRING ( https://string-db.org ). Dark green and pink lines represent known interactions. Green, red, and blue lines represent predicted interactions. Light green, black and gray lines represent other interactions. ( B and C ) Spearman correlation ( B ), the circle size represents correlation strength, and the color represents the positive (orange) or negative (blue) correlation and the univariate Cox regression analyses ( C ) of secretory pathway-related genes in TCGA glioma RNA-seq dataset. ( D ) Oncomine analysis of FAM20C expression in indicated cancers (The number in red represents the number of datasets demonstrating elevated FAM20C expression in indicated cancers. The number in blue represents the number of datasets showing decreased FAM20C expression in indicated cancers. The intensity of the color means the level of P value).

Journal: OncoTargets and therapy

Article Title: Secretory Pathway Kinase FAM20C , a Marker for Glioma Invasion and Malignancy, Predicts Poor Prognosis of Glioma

doi: 10.2147/OTT.S275452

Figure Lengend Snippet: Integrative transcriptomic analyses identify FAM20C as the core member of secretory pathway kinase or kinase-like proteins (SPKKPs) family in glioma. ( A ) The protein interaction analysis among SPKKPs member genes with STRING ( https://string-db.org ). Dark green and pink lines represent known interactions. Green, red, and blue lines represent predicted interactions. Light green, black and gray lines represent other interactions. ( B and C ) Spearman correlation ( B ), the circle size represents correlation strength, and the color represents the positive (orange) or negative (blue) correlation and the univariate Cox regression analyses ( C ) of secretory pathway-related genes in TCGA glioma RNA-seq dataset. ( D ) Oncomine analysis of FAM20C expression in indicated cancers (The number in red represents the number of datasets demonstrating elevated FAM20C expression in indicated cancers. The number in blue represents the number of datasets showing decreased FAM20C expression in indicated cancers. The intensity of the color means the level of P value).

Article Snippet: Figure 4 FAM20C knockdown suppresses the migration, invasion, and colony formation of GBM cells. ( A ) GSEA with TCGA GBM RNA-seq dataset disclosed a significant enrichment of cell adhesion- and immune response-related phenotypes in GBM patients with high FAM20C expression. ( B ) Heat maps describing the association between FAM20C expression and cell adhesion and negative immune-regulation‐related genes in TCGA GBM RNA-seq dataset (Chi-square test). ( C ) KEGG analysis was performed on genes with a correlation coefficient greater than 0.3 (Spearman analysis) with FAM20C in TCGA GBM RNA-seq dataset. ( D ) qPCR analyses of FAM20C, MMP2, and MMP9 mRNA expression in LN229 cells transfected with siRNA targeting FAM20C or a non-targeting control (n = 4, t -test). ( E ) Transwell assays demonstrating FAM20C knock-down inhibited the migration (upper panel) and invasion (lower panel) capabilities of LN229 cells (n = 10, t -test). ( ) The colony formation assay showing FAM20C knock-down significantly inhibited the colony formation capability of LN229 cells. (n = 6, t -test). (* P < 0.05, *** P < 0.001, and **** P < 0.0001).

Techniques: RNA Sequencing, Expressing

FAM20C is associated with progressive malignancy and unfavorable prognosis in glioma. ( A ) Representative immunohistochemical images of FAM20C staining in clinical glioma samples (Scale bar, 50 μm; grade II: n = 3, grade III: n = 7, grade IV: n=28). ( B ) Kaplan–Meier curve evaluating the correlation between FAM20C protein expression and GBM patients’ survival (FAM20C low vs high, low n = 11, high n = 17, P = 0.0241; Log rank test). ( C ) The analyses of FAM20C expression in non-tumor and different grade glioma samples (TCGA glioma RNA-seq: non-tumor, n = 5; grade II: n = 130; grade III: n = 133; GBM: n = 80, one-way ANOVA). ( D ) Kaplan–Meier curves of FAM20C expression and the survival of different grade glioma in TCGA. (left panel: grade II, low n = 130, high n = 130, P = 0.710; middle panel: grade III, low n = 133, high n = 134, P = 0.0069; right panel: GBM, low n = 80, high n = 80, P = 0.0013, Log rank test). ( E ) The analysis of FAM20C expression in non-tumor and GBM samples using data from Clinical Proteomic Tumor Analysis Consortium (CPTAC, non-tumor, n = 10; GBM, n = 100, P < 0.0001, t -test). ( F ) Kaplan–Meier curves of FAM20C expression and GBM patients’ survival in CPTAC. (high: n = 46, low: n = 47, P = 0.0032, Log rank test) ( G ) The analysis of FAM20C expression in different regions of GBM with data from the IVY GBM Altas Project ( http://glioblastoma.alleninstitute.org/ ). ( H ) Representative immunohistochemical images of FAM20C staining in peri-necrotic region of GBM. (Scale bar, 50 μm). ( I ) The analyses of FAM20C expression in different subtypes GBM (TCGA GBM RNA-seq: classical n = 48; mesenchymal n = 65; proneural n = 18, one-way ANOVA). ( J ) The receiver operator characteristic (ROC) curve describing the sensitivity and specificity of FAM20C as a marker for mesenchymal (n = 65) vs other subtypes (classical n = 48, and proneural n = 18) in TCGA. ( K ) FAM20C expression analysis in GBM with different IDH status (TCGA GBM RNA-seq: IDH mut, n = 11; IDH wt, n = 143, P < 0.0001, t -test). ( L ) Kaplan‐Meier curve describing the association between FAM20C expression and GBM IDH wt patients’ 2-year survival (TCGA, low n = 71, high n = 71, P = 0.0288, Log rank test). ( M ) The ROC curves comparing the sensitivity and specificity of FAM20C as a prognostic marker for glioma patients in TCGA (left panel: 3‐year; right: 5‐year). ( N and O ) Kaplan–Meier curves describing the association between FAM20C expression and TCGA GBM patients’ survival with or without radiation ( N ) or chemotherapy ( O ) (N: low without radiation n = 39, low with radiation n = 43; high without radiation n = 32, high with radiation n = 50; ( O ) low without chemotherapy n = 22, low with chemotherapy n = 61; high without chemotherapy n = 18, high with chemotherapy n = 66, Log rank test). (ns P > 0.05, * P < 0.05, ** P < 0.01, *** P < 0.001, and **** P < 0.0001).

Journal: OncoTargets and therapy

Article Title: Secretory Pathway Kinase FAM20C , a Marker for Glioma Invasion and Malignancy, Predicts Poor Prognosis of Glioma

doi: 10.2147/OTT.S275452

Figure Lengend Snippet: FAM20C is associated with progressive malignancy and unfavorable prognosis in glioma. ( A ) Representative immunohistochemical images of FAM20C staining in clinical glioma samples (Scale bar, 50 μm; grade II: n = 3, grade III: n = 7, grade IV: n=28). ( B ) Kaplan–Meier curve evaluating the correlation between FAM20C protein expression and GBM patients’ survival (FAM20C low vs high, low n = 11, high n = 17, P = 0.0241; Log rank test). ( C ) The analyses of FAM20C expression in non-tumor and different grade glioma samples (TCGA glioma RNA-seq: non-tumor, n = 5; grade II: n = 130; grade III: n = 133; GBM: n = 80, one-way ANOVA). ( D ) Kaplan–Meier curves of FAM20C expression and the survival of different grade glioma in TCGA. (left panel: grade II, low n = 130, high n = 130, P = 0.710; middle panel: grade III, low n = 133, high n = 134, P = 0.0069; right panel: GBM, low n = 80, high n = 80, P = 0.0013, Log rank test). ( E ) The analysis of FAM20C expression in non-tumor and GBM samples using data from Clinical Proteomic Tumor Analysis Consortium (CPTAC, non-tumor, n = 10; GBM, n = 100, P < 0.0001, t -test). ( F ) Kaplan–Meier curves of FAM20C expression and GBM patients’ survival in CPTAC. (high: n = 46, low: n = 47, P = 0.0032, Log rank test) ( G ) The analysis of FAM20C expression in different regions of GBM with data from the IVY GBM Altas Project ( http://glioblastoma.alleninstitute.org/ ). ( H ) Representative immunohistochemical images of FAM20C staining in peri-necrotic region of GBM. (Scale bar, 50 μm). ( I ) The analyses of FAM20C expression in different subtypes GBM (TCGA GBM RNA-seq: classical n = 48; mesenchymal n = 65; proneural n = 18, one-way ANOVA). ( J ) The receiver operator characteristic (ROC) curve describing the sensitivity and specificity of FAM20C as a marker for mesenchymal (n = 65) vs other subtypes (classical n = 48, and proneural n = 18) in TCGA. ( K ) FAM20C expression analysis in GBM with different IDH status (TCGA GBM RNA-seq: IDH mut, n = 11; IDH wt, n = 143, P < 0.0001, t -test). ( L ) Kaplan‐Meier curve describing the association between FAM20C expression and GBM IDH wt patients’ 2-year survival (TCGA, low n = 71, high n = 71, P = 0.0288, Log rank test). ( M ) The ROC curves comparing the sensitivity and specificity of FAM20C as a prognostic marker for glioma patients in TCGA (left panel: 3‐year; right: 5‐year). ( N and O ) Kaplan–Meier curves describing the association between FAM20C expression and TCGA GBM patients’ survival with or without radiation ( N ) or chemotherapy ( O ) (N: low without radiation n = 39, low with radiation n = 43; high without radiation n = 32, high with radiation n = 50; ( O ) low without chemotherapy n = 22, low with chemotherapy n = 61; high without chemotherapy n = 18, high with chemotherapy n = 66, Log rank test). (ns P > 0.05, * P < 0.05, ** P < 0.01, *** P < 0.001, and **** P < 0.0001).

Article Snippet: Figure 4 FAM20C knockdown suppresses the migration, invasion, and colony formation of GBM cells. ( A ) GSEA with TCGA GBM RNA-seq dataset disclosed a significant enrichment of cell adhesion- and immune response-related phenotypes in GBM patients with high FAM20C expression. ( B ) Heat maps describing the association between FAM20C expression and cell adhesion and negative immune-regulation‐related genes in TCGA GBM RNA-seq dataset (Chi-square test). ( C ) KEGG analysis was performed on genes with a correlation coefficient greater than 0.3 (Spearman analysis) with FAM20C in TCGA GBM RNA-seq dataset. ( D ) qPCR analyses of FAM20C, MMP2, and MMP9 mRNA expression in LN229 cells transfected with siRNA targeting FAM20C or a non-targeting control (n = 4, t -test). ( E ) Transwell assays demonstrating FAM20C knock-down inhibited the migration (upper panel) and invasion (lower panel) capabilities of LN229 cells (n = 10, t -test). ( ) The colony formation assay showing FAM20C knock-down significantly inhibited the colony formation capability of LN229 cells. (n = 6, t -test). (* P < 0.05, *** P < 0.001, and **** P < 0.0001).

Techniques: Immunohistochemical staining, Staining, Expressing, RNA Sequencing, Marker

FAM20C knockdown suppresses the migration, invasion, and colony formation of GBM cells. ( A ) GSEA with TCGA GBM RNA-seq dataset disclosed a significant enrichment of cell adhesion- and immune response-related phenotypes in GBM patients with high FAM20C expression. ( B ) Heat maps describing the association between FAM20C expression and cell adhesion and negative immune-regulation‐related genes in TCGA GBM RNA-seq dataset (Chi-square test). ( C ) KEGG analysis was performed on genes with a correlation coefficient greater than 0.3 (Spearman analysis) with FAM20C in TCGA GBM RNA-seq dataset. ( D ) qPCR analyses of FAM20C, MMP2, and MMP9 mRNA expression in LN229 cells transfected with siRNA targeting FAM20C or a non-targeting control (n = 4, t -test). ( E ) Transwell assays demonstrating FAM20C knock-down inhibited the migration (upper panel) and invasion (lower panel) capabilities of LN229 cells (n = 10, t -test). ( F ) The colony formation assay showing FAM20C knock-down significantly inhibited the colony formation capability of LN229 cells. (n = 6, t -test). (* P < 0.05, *** P < 0.001, and **** P < 0.0001).

Journal: OncoTargets and therapy

Article Title: Secretory Pathway Kinase FAM20C , a Marker for Glioma Invasion and Malignancy, Predicts Poor Prognosis of Glioma

doi: 10.2147/OTT.S275452

Figure Lengend Snippet: FAM20C knockdown suppresses the migration, invasion, and colony formation of GBM cells. ( A ) GSEA with TCGA GBM RNA-seq dataset disclosed a significant enrichment of cell adhesion- and immune response-related phenotypes in GBM patients with high FAM20C expression. ( B ) Heat maps describing the association between FAM20C expression and cell adhesion and negative immune-regulation‐related genes in TCGA GBM RNA-seq dataset (Chi-square test). ( C ) KEGG analysis was performed on genes with a correlation coefficient greater than 0.3 (Spearman analysis) with FAM20C in TCGA GBM RNA-seq dataset. ( D ) qPCR analyses of FAM20C, MMP2, and MMP9 mRNA expression in LN229 cells transfected with siRNA targeting FAM20C or a non-targeting control (n = 4, t -test). ( E ) Transwell assays demonstrating FAM20C knock-down inhibited the migration (upper panel) and invasion (lower panel) capabilities of LN229 cells (n = 10, t -test). ( F ) The colony formation assay showing FAM20C knock-down significantly inhibited the colony formation capability of LN229 cells. (n = 6, t -test). (* P < 0.05, *** P < 0.001, and **** P < 0.0001).

Article Snippet: Figure 4 FAM20C knockdown suppresses the migration, invasion, and colony formation of GBM cells. ( A ) GSEA with TCGA GBM RNA-seq dataset disclosed a significant enrichment of cell adhesion- and immune response-related phenotypes in GBM patients with high FAM20C expression. ( B ) Heat maps describing the association between FAM20C expression and cell adhesion and negative immune-regulation‐related genes in TCGA GBM RNA-seq dataset (Chi-square test). ( C ) KEGG analysis was performed on genes with a correlation coefficient greater than 0.3 (Spearman analysis) with FAM20C in TCGA GBM RNA-seq dataset. ( D ) qPCR analyses of FAM20C, MMP2, and MMP9 mRNA expression in LN229 cells transfected with siRNA targeting FAM20C or a non-targeting control (n = 4, t -test). ( E ) Transwell assays demonstrating FAM20C knock-down inhibited the migration (upper panel) and invasion (lower panel) capabilities of LN229 cells (n = 10, t -test). ( ) The colony formation assay showing FAM20C knock-down significantly inhibited the colony formation capability of LN229 cells. (n = 6, t -test). (* P < 0.05, *** P < 0.001, and **** P < 0.0001).

Techniques: Knockdown, Migration, RNA Sequencing, Expressing, Transfection, Control, Colony Assay

The screening of FAM20C substrates in GBM distinguishes FN1 as the key substrate interacting with it. ( A ) The analyses of protein interactions between FAM20C and its substrates by STRING webtool ( https://string-db.org ). ( B ) The univariate Cox regression analyses of FAM20C substrates in TCGA GBM RNA-seq dataset. ( C ) Pearson correlation analysis between FAM20C and its substrates (TCGA GBM RNA-seq dataset: circle size represents the correlation strength, and color represents the positive (orange) or negative (blue) correlation). ( D ) Pearson correlation analysis between FAM20C and FN1 in TCGA GBM RNA-seq dataset. ( E ) Three-dimensional binding pattern of FAM20C and FN1 protein obtained by molecular docking simulation. ( F ) The interaction site between FAM20C and FN1.

Journal: OncoTargets and therapy

Article Title: Secretory Pathway Kinase FAM20C , a Marker for Glioma Invasion and Malignancy, Predicts Poor Prognosis of Glioma

doi: 10.2147/OTT.S275452

Figure Lengend Snippet: The screening of FAM20C substrates in GBM distinguishes FN1 as the key substrate interacting with it. ( A ) The analyses of protein interactions between FAM20C and its substrates by STRING webtool ( https://string-db.org ). ( B ) The univariate Cox regression analyses of FAM20C substrates in TCGA GBM RNA-seq dataset. ( C ) Pearson correlation analysis between FAM20C and its substrates (TCGA GBM RNA-seq dataset: circle size represents the correlation strength, and color represents the positive (orange) or negative (blue) correlation). ( D ) Pearson correlation analysis between FAM20C and FN1 in TCGA GBM RNA-seq dataset. ( E ) Three-dimensional binding pattern of FAM20C and FN1 protein obtained by molecular docking simulation. ( F ) The interaction site between FAM20C and FN1.

Article Snippet: Figure 4 FAM20C knockdown suppresses the migration, invasion, and colony formation of GBM cells. ( A ) GSEA with TCGA GBM RNA-seq dataset disclosed a significant enrichment of cell adhesion- and immune response-related phenotypes in GBM patients with high FAM20C expression. ( B ) Heat maps describing the association between FAM20C expression and cell adhesion and negative immune-regulation‐related genes in TCGA GBM RNA-seq dataset (Chi-square test). ( C ) KEGG analysis was performed on genes with a correlation coefficient greater than 0.3 (Spearman analysis) with FAM20C in TCGA GBM RNA-seq dataset. ( D ) qPCR analyses of FAM20C, MMP2, and MMP9 mRNA expression in LN229 cells transfected with siRNA targeting FAM20C or a non-targeting control (n = 4, t -test). ( E ) Transwell assays demonstrating FAM20C knock-down inhibited the migration (upper panel) and invasion (lower panel) capabilities of LN229 cells (n = 10, t -test). ( ) The colony formation assay showing FAM20C knock-down significantly inhibited the colony formation capability of LN229 cells. (n = 6, t -test). (* P < 0.05, *** P < 0.001, and **** P < 0.0001).

Techniques: RNA Sequencing, Binding Assay

FAM20C is associated with the regulation of immune response in GBM. ( A ) Heat maps describing the expression of FAM20C negatively correlated with tumor purity, and positively correlated with immune score and stromal score (TCGA GBM RNA-seq, n = 168, Pearson correlation analysis). ( B ) The analyses of xCell (upper panel) and EPIC scores (lower panel) indicating FAM20C expression pattern in indicated cell populations (TCGA GBM RNA-seq: FAM20C low n = 84; high n = 84, t -test). ( C and D ) t-SNE map color-coded for transcript counts ( C ) and the corresponding single-cell bar plots ( D ) of FAM20C enriched in different cell subpopulations of GBM (SCP393 dataset, https://portals.broadinstitute.org/single_cell/study/SCP393/single-cell-rna-seq-of-adult-and-pediatric-glioblastoma ). ( E ) Transwell assay showing 20 μg/mL FAM20C recombinant protein significantly enhances the migration of THP1 cells. (n = 15, t -test). (ns P > 0.05, ** P < 0.01, *** P < 0.001, and **** P < 0.0001).

Journal: OncoTargets and therapy

Article Title: Secretory Pathway Kinase FAM20C , a Marker for Glioma Invasion and Malignancy, Predicts Poor Prognosis of Glioma

doi: 10.2147/OTT.S275452

Figure Lengend Snippet: FAM20C is associated with the regulation of immune response in GBM. ( A ) Heat maps describing the expression of FAM20C negatively correlated with tumor purity, and positively correlated with immune score and stromal score (TCGA GBM RNA-seq, n = 168, Pearson correlation analysis). ( B ) The analyses of xCell (upper panel) and EPIC scores (lower panel) indicating FAM20C expression pattern in indicated cell populations (TCGA GBM RNA-seq: FAM20C low n = 84; high n = 84, t -test). ( C and D ) t-SNE map color-coded for transcript counts ( C ) and the corresponding single-cell bar plots ( D ) of FAM20C enriched in different cell subpopulations of GBM (SCP393 dataset, https://portals.broadinstitute.org/single_cell/study/SCP393/single-cell-rna-seq-of-adult-and-pediatric-glioblastoma ). ( E ) Transwell assay showing 20 μg/mL FAM20C recombinant protein significantly enhances the migration of THP1 cells. (n = 15, t -test). (ns P > 0.05, ** P < 0.01, *** P < 0.001, and **** P < 0.0001).

Article Snippet: Figure 4 FAM20C knockdown suppresses the migration, invasion, and colony formation of GBM cells. ( A ) GSEA with TCGA GBM RNA-seq dataset disclosed a significant enrichment of cell adhesion- and immune response-related phenotypes in GBM patients with high FAM20C expression. ( B ) Heat maps describing the association between FAM20C expression and cell adhesion and negative immune-regulation‐related genes in TCGA GBM RNA-seq dataset (Chi-square test). ( C ) KEGG analysis was performed on genes with a correlation coefficient greater than 0.3 (Spearman analysis) with FAM20C in TCGA GBM RNA-seq dataset. ( D ) qPCR analyses of FAM20C, MMP2, and MMP9 mRNA expression in LN229 cells transfected with siRNA targeting FAM20C or a non-targeting control (n = 4, t -test). ( E ) Transwell assays demonstrating FAM20C knock-down inhibited the migration (upper panel) and invasion (lower panel) capabilities of LN229 cells (n = 10, t -test). ( ) The colony formation assay showing FAM20C knock-down significantly inhibited the colony formation capability of LN229 cells. (n = 6, t -test). (* P < 0.05, *** P < 0.001, and **** P < 0.0001).

Techniques: Expressing, RNA Sequencing, Transwell Assay, Recombinant, Migration