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Shanghai Genechem Ltd transcriptomic sequencing
Transcriptomic Sequencing, supplied by Shanghai Genechem Ltd, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/transcriptomic+sequencing/sequencing+transcriptome/pm42265076-309-0-9
Average 86 stars, based on 1 article reviews
transcriptomic sequencing - by Bioz Stars, 2026-10
86/100 stars

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Transcriptomics:

Article Title: Activation of YBX1 and JAK2/STAT3 pathways by RIOK1 increases lenvatinib resistance in hepatocellular carcinoma cells.
Article Snippet: Research Paper Activation of YBX1 and JAK2/STAT3 pathways by RIOK1 increases lenvatinib resistance in hepatocellular carcinoma cells Ruirui Cao a,1, Yue Wu a,1, Yawei Li a, Mengting Shi a, Tiantian Zhang a, Weijie Sun a, Yang Liu a, Rui Wang a,b,* a Department of Medical Oncology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, 233004, Anhui, PR China b Anhui Provincial Key Laboratory of Tumor Evolution and Intelligent Diagnosis and Treatment, Bengbu Medical University, Bengbu, 233004, Anhui, PR China A R T I C L E I N F O Keywords: RIOK1 YBX1 JAK2/STAT3 Hepatocellular carcinoma Lenvatinib resistance A B S T R A C T Hepatocellular carcinoma (HCC) is fatal and does not respond well to classical chemotherapies.. This paper investigates the molecular mechanism of RIOK1 on lenvatinib resistance in HCC cells.. Differentially expressed genes were identified before and after lenvatinib treatment using transcriptome sequencing.

Sequencing:

Article Title: Activation of YBX1 and JAK2/STAT3 pathways by RIOK1 increases lenvatinib resistance in hepatocellular carcinoma cells.
Article Snippet: Research Paper Activation of YBX1 and JAK2/STAT3 pathways by RIOK1 increases lenvatinib resistance in hepatocellular carcinoma cells Ruirui Cao a,1, Yue Wu a,1, Yawei Li a, Mengting Shi a, Tiantian Zhang a, Weijie Sun a, Yang Liu a, Rui Wang a,b,* a Department of Medical Oncology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, 233004, Anhui, PR China b Anhui Provincial Key Laboratory of Tumor Evolution and Intelligent Diagnosis and Treatment, Bengbu Medical University, Bengbu, 233004, Anhui, PR China A R T I C L E I N F O Keywords: RIOK1 YBX1 JAK2/STAT3 Hepatocellular carcinoma Lenvatinib resistance A B S T R A C T Hepatocellular carcinoma (HCC) is fatal and does not respond well to classical chemotherapies.. This paper investigates the molecular mechanism of RIOK1 on lenvatinib resistance in HCC cells.. Differentially expressed genes were identified before and after lenvatinib treatment using transcriptome sequencing.

Article Title: Unraveling the neuroprotective mechanism of gastrodin against CRS-induced depression in adult male mice via Notch3: Integration of proteomics, transcriptomics, molecular docking, and cellular validation.
Article Snippet: Original Article Unraveling the neuroprotective mechanism of gastrodin against CRS-induced depression in adult male mice via Notch3: Integration of proteomics, transcriptomics, molecular docking, and cellular validation Hongchun Xiang a,* , Chen Tan a, Man Long a, Jin Zhang b, Xinyi Zou b, Guowei Cai a, Jing Li a,* a Department of Acupuncture-Moxibustion, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No.1277 Jiefang Ave, Wuhan, Hubei 430022, PR China b College of Acupuncture and Moxibustion and Orthopedics, Hubei University of Chinese Medicine, Wuhan 430061, PR China A R T I C L E I N F O Keywords: Gastrodin Depression Chronic restraint stress Medial prefrontal cortex Notch signaling pathway Notch3 Background: Major Depressive Disorder (MDD) affects hundreds of millions of people worldwide.. Numerous studies have identified the medial prefrontal cortex (mPFC) as a key brain region involved in regulating depression.. Clarifying how to modulate the mPFC can improve our understanding of its role in depression.

Article Title: Aging-dependent microglial heterogeneity worsens outcomes in models of traumatic brain injury
Article Snippet: .. 727 728 Single-cell RNA-sequencing library preparation and sequencing 729 Single-cell RNA-sequencing was conducted by Shanghai GeneChem Co., Ltd. .. In brief, cell 730 suspensions were processed to generate single-cell RNA-Seq libraries using the Chromium 731 Next GEM Single Cell 5′ GEM, Library, and Gel Bead Kit v3.1 (10x Genomics, Pleasanton, 732 CA, USA) according to the manufacturer’s protocol, as outlined below.

Article Title: Growth differentiation factor 11 attenuates sepsis-associated acute kidney injury by reducing inflammation and coagulation via PGC-1α/Nrf2 activation.
Article Snippet: .. Transcriptome sequencing and analysis were conducted by Shanghai Genechem Co., Ltd. .. The transcriptomic data have been submitted to the National Center for Biotechnology Information Gene Expression Omnibus under accession number GSE297658.

Article Title: N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine-quinone (6PPD-Q) induces osteoporosis by a mechanism involving CX43-mediated mitochondrial autophagy dysfunction in bone marrow mesenchymal stem cells.
Article Snippet: .. Four rat tibial tissues were respectively selected from the NC group and the 6PPD-Q (High-dose) group for transcriptome sequencing (Shanghai Genechem Co., Ltd.). ..

Article Title: Metabolomic profiling delineates the role of adenosine in oocyte quality and embryonic development.
Article Snippet: .. Transcriptomic sequencing of the oocyte samples was supplied by Shanghai Genechem Chemical Technology Co., Ltd. (Shanghai, China). ..

Article Title: Aging-dependent microglial heterogeneity worsens outcomes in models of traumatic brain injury.
Article Snippet: .. 727 728 Single-cell RNA-sequencing library preparation and sequencing 729 Single-cell RNA-sequencing was conducted by Shanghai GeneChem Co., Ltd. .. In brief, cell 730 suspensions were processed to generate single-cell RNA-Seq libraries using the Chromium 731 Next GEM Single Cell 5′ GEM, Library, and Gel Bead Kit v3.1 (10x Genomics, Pleasanton, 732 CA, USA) according to the manufacturer’s protocol, as outlined below.

Phospho-proteomics:

Article Title: Activation of YBX1 and JAK2/STAT3 pathways by RIOK1 increases lenvatinib resistance in hepatocellular carcinoma cells.
Article Snippet: Research Paper Activation of YBX1 and JAK2/STAT3 pathways by RIOK1 increases lenvatinib resistance in hepatocellular carcinoma cells Ruirui Cao a,1, Yue Wu a,1, Yawei Li a, Mengting Shi a, Tiantian Zhang a, Weijie Sun a, Yang Liu a, Rui Wang a,b,* a Department of Medical Oncology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, 233004, Anhui, PR China b Anhui Provincial Key Laboratory of Tumor Evolution and Intelligent Diagnosis and Treatment, Bengbu Medical University, Bengbu, 233004, Anhui, PR China A R T I C L E I N F O Keywords: RIOK1 YBX1 JAK2/STAT3 Hepatocellular carcinoma Lenvatinib resistance A B S T R A C T Hepatocellular carcinoma (HCC) is fatal and does not respond well to classical chemotherapies.. This paper investigates the molecular mechanism of RIOK1 on lenvatinib resistance in HCC cells.. Differentially expressed genes were identified before and after lenvatinib treatment using transcriptome sequencing.

Single Cell:

Article Title: Aging-dependent microglial heterogeneity worsens outcomes in models of traumatic brain injury
Article Snippet: .. 727 728 Single-cell RNA-sequencing library preparation and sequencing 729 Single-cell RNA-sequencing was conducted by Shanghai GeneChem Co., Ltd. .. In brief, cell 730 suspensions were processed to generate single-cell RNA-Seq libraries using the Chromium 731 Next GEM Single Cell 5′ GEM, Library, and Gel Bead Kit v3.1 (10x Genomics, Pleasanton, 732 CA, USA) according to the manufacturer’s protocol, as outlined below.

Article Title: Aging-dependent microglial heterogeneity worsens outcomes in models of traumatic brain injury.
Article Snippet: .. 727 728 Single-cell RNA-sequencing library preparation and sequencing 729 Single-cell RNA-sequencing was conducted by Shanghai GeneChem Co., Ltd. .. In brief, cell 730 suspensions were processed to generate single-cell RNA-Seq libraries using the Chromium 731 Next GEM Single Cell 5′ GEM, Library, and Gel Bead Kit v3.1 (10x Genomics, Pleasanton, 732 CA, USA) according to the manufacturer’s protocol, as outlined below.

RNA sequencing:

Article Title: Aging-dependent microglial heterogeneity worsens outcomes in models of traumatic brain injury
Article Snippet: .. 727 728 Single-cell RNA-sequencing library preparation and sequencing 729 Single-cell RNA-sequencing was conducted by Shanghai GeneChem Co., Ltd. .. In brief, cell 730 suspensions were processed to generate single-cell RNA-Seq libraries using the Chromium 731 Next GEM Single Cell 5′ GEM, Library, and Gel Bead Kit v3.1 (10x Genomics, Pleasanton, 732 CA, USA) according to the manufacturer’s protocol, as outlined below.

Article Title: Aging-dependent microglial heterogeneity worsens outcomes in models of traumatic brain injury.
Article Snippet: .. 727 728 Single-cell RNA-sequencing library preparation and sequencing 729 Single-cell RNA-sequencing was conducted by Shanghai GeneChem Co., Ltd. .. In brief, cell 730 suspensions were processed to generate single-cell RNA-Seq libraries using the Chromium 731 Next GEM Single Cell 5′ GEM, Library, and Gel Bead Kit v3.1 (10x Genomics, Pleasanton, 732 CA, USA) according to the manufacturer’s protocol, as outlined below.

Transfection:

Article Title: Protective Effects of Neuron-Derived Quiescin Sulfhydryl Oxidase 1 Protein on Intracerebral Hemorrhage
Article Snippet: .. After 48 h, the transfected cells underwent transcriptome analysis (Shanghai Genechem Co., Ltd., Shanghai, China). ..



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<t>Transcriptomic</t> and TME characteristics associated with ECMSig in TCGA-GBM cohort (A) Volcano plot showing DEGs between ECMSig-high and ECMSig-low groups. Red dots: upregulated in high-risk; blue dots: upregulated in low-risk. Benjamini-Hochberg adjusted. (B) Gene set enrichment analysis (GSEA) plots showing enrichment of hallmark pathways. Pathways enriched in ECMSig-high and ECMSig-low groups are shown with their running enrichment scores (ESs) and ranked gene lists. Benjamini-Hochberg adjusted. (C) Heatmap showing the activity scores of selected oncogenic and tumor-related signaling pathways (rows) across TCGA-GBM samples (columns), annotated by ECMSig group and ECMSig score. Red indicates high activity, blue indicates low activity. ∗ p < 0.05. Wilcoxon signed-rank test. (D) Heatmap depicting the estimated infiltration levels of various immune and stromal cell types (rows) in TCGA-GBM samples (columns), stratified by ECMSig group and score. Red indicates high infiltration, blue indicates low infiltration. Cells significantly highly infiltrated in ECMSig-high are labeled in red, and those high in ECMSig-low group are in blue. ∗q < 0.05, ∗∗q < 0.01, ∗∗∗q < 0.001. Wilcoxon signed-rank test. Benjamini-Hochberg adjusted. (E and F) Scatterplots showing the spearman correlation between ECMSig score and (E) Macrophage_XCELL infiltration score and (F) immune_score_XCELL. The blue line represents the linear regression fit with 95% confidence interval bands. Spearman correlation test.
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Image Search Results


Transcriptomic and TME characteristics associated with ECMSig in TCGA-GBM cohort (A) Volcano plot showing DEGs between ECMSig-high and ECMSig-low groups. Red dots: upregulated in high-risk; blue dots: upregulated in low-risk. Benjamini-Hochberg adjusted. (B) Gene set enrichment analysis (GSEA) plots showing enrichment of hallmark pathways. Pathways enriched in ECMSig-high and ECMSig-low groups are shown with their running enrichment scores (ESs) and ranked gene lists. Benjamini-Hochberg adjusted. (C) Heatmap showing the activity scores of selected oncogenic and tumor-related signaling pathways (rows) across TCGA-GBM samples (columns), annotated by ECMSig group and ECMSig score. Red indicates high activity, blue indicates low activity. ∗ p < 0.05. Wilcoxon signed-rank test. (D) Heatmap depicting the estimated infiltration levels of various immune and stromal cell types (rows) in TCGA-GBM samples (columns), stratified by ECMSig group and score. Red indicates high infiltration, blue indicates low infiltration. Cells significantly highly infiltrated in ECMSig-high are labeled in red, and those high in ECMSig-low group are in blue. ∗q < 0.05, ∗∗q < 0.01, ∗∗∗q < 0.001. Wilcoxon signed-rank test. Benjamini-Hochberg adjusted. (E and F) Scatterplots showing the spearman correlation between ECMSig score and (E) Macrophage_XCELL infiltration score and (F) immune_score_XCELL. The blue line represents the linear regression fit with 95% confidence interval bands. Spearman correlation test.

Journal: iScience

Article Title: Multi-omics profiling-derived signature links cellular ecosystem to glioblastoma prognosis

doi: 10.1016/j.isci.2026.115982

Figure Lengend Snippet: Transcriptomic and TME characteristics associated with ECMSig in TCGA-GBM cohort (A) Volcano plot showing DEGs between ECMSig-high and ECMSig-low groups. Red dots: upregulated in high-risk; blue dots: upregulated in low-risk. Benjamini-Hochberg adjusted. (B) Gene set enrichment analysis (GSEA) plots showing enrichment of hallmark pathways. Pathways enriched in ECMSig-high and ECMSig-low groups are shown with their running enrichment scores (ESs) and ranked gene lists. Benjamini-Hochberg adjusted. (C) Heatmap showing the activity scores of selected oncogenic and tumor-related signaling pathways (rows) across TCGA-GBM samples (columns), annotated by ECMSig group and ECMSig score. Red indicates high activity, blue indicates low activity. ∗ p < 0.05. Wilcoxon signed-rank test. (D) Heatmap depicting the estimated infiltration levels of various immune and stromal cell types (rows) in TCGA-GBM samples (columns), stratified by ECMSig group and score. Red indicates high infiltration, blue indicates low infiltration. Cells significantly highly infiltrated in ECMSig-high are labeled in red, and those high in ECMSig-low group are in blue. ∗q < 0.05, ∗∗q < 0.01, ∗∗∗q < 0.001. Wilcoxon signed-rank test. Benjamini-Hochberg adjusted. (E and F) Scatterplots showing the spearman correlation between ECMSig score and (E) Macrophage_XCELL infiltration score and (F) immune_score_XCELL. The blue line represents the linear regression fit with 95% confidence interval bands. Spearman correlation test.

Article Snippet: The single-cell transcriptomic sequencing dataset utilizing technology from the 10X Genomics platform was available under the accession number GEO: GSE182109 at the Gene Expression Omnibus (GEO) repository.

Techniques: Activity Assay, Protein-Protein interactions, Labeling

Single-cell RNA sequencing analysis revealing ECMSig expression across cell types and identification of prognostically relevant cell states in GBM (A) UMAP visualization of major cell types identified in GBM scRNA-seq data. (B) Dot plot showing the scaled average expression (color intensity) and percentage of cells expressing (dot size) canonical marker genes for each major cell type. (C) Dot plot showing the scaled average expression and percentage of cells expressing the seven ECMSig genes across major cell types. (D) UMAP plots showing the expression levels of individual ECMSig genes and overall ECMSig score across all cells. (E–G) UMAP plots illustrating Scissor-identified prognostically unfavorable (Scissor_Pos, red dashed circle) and favorable (Scissor_Neg, blue dashed circle; Scissor_Others, gray) cell subpopulations within (E) tumor cells, (F) myeloid cells, and (G) endothelial cells. (H–K) Violin plots comparing ECMSig scores among tumor cells grouped by Scissor status (H) and tumor type (I), and myeloid cells (J) or endothelial cells (K) grouped by Scissor status. ∗∗∗∗ p < 0.0001. Wilcoxon signed-rank test. (L) Dot plot showing differentially expressed marker genes between myeloid Scissor_Pos and other myeloid cells. Dot size indicates the fraction of cells in the group expressing the gene; color indicates average expression level.

Journal: iScience

Article Title: Multi-omics profiling-derived signature links cellular ecosystem to glioblastoma prognosis

doi: 10.1016/j.isci.2026.115982

Figure Lengend Snippet: Single-cell RNA sequencing analysis revealing ECMSig expression across cell types and identification of prognostically relevant cell states in GBM (A) UMAP visualization of major cell types identified in GBM scRNA-seq data. (B) Dot plot showing the scaled average expression (color intensity) and percentage of cells expressing (dot size) canonical marker genes for each major cell type. (C) Dot plot showing the scaled average expression and percentage of cells expressing the seven ECMSig genes across major cell types. (D) UMAP plots showing the expression levels of individual ECMSig genes and overall ECMSig score across all cells. (E–G) UMAP plots illustrating Scissor-identified prognostically unfavorable (Scissor_Pos, red dashed circle) and favorable (Scissor_Neg, blue dashed circle; Scissor_Others, gray) cell subpopulations within (E) tumor cells, (F) myeloid cells, and (G) endothelial cells. (H–K) Violin plots comparing ECMSig scores among tumor cells grouped by Scissor status (H) and tumor type (I), and myeloid cells (J) or endothelial cells (K) grouped by Scissor status. ∗∗∗∗ p < 0.0001. Wilcoxon signed-rank test. (L) Dot plot showing differentially expressed marker genes between myeloid Scissor_Pos and other myeloid cells. Dot size indicates the fraction of cells in the group expressing the gene; color indicates average expression level.

Article Snippet: The single-cell transcriptomic sequencing dataset utilizing technology from the 10X Genomics platform was available under the accession number GEO: GSE182109 at the Gene Expression Omnibus (GEO) repository.

Techniques: Single Cell, RNA Sequencing, Expressing, Marker

Spatial transcriptomic analysis revealing co-localization of ECMSig, hypoxia, Scissor-Positive cells, and pericytes in GBM (A) Spatial feature plots for four GBM samples. Each row represents a sample. Columns show spatial heatmaps of: ECMSig score, hypoxia signature score, tumor Scissor_Pos signature score, myeloid Scissor_Pos signature score, endothelial Scissor Pos signature score, and pericyte marker signature score. Color scale indicates scaled expression or score (low to high). Each dot represents a spatial barcoded spot.

Journal: iScience

Article Title: Multi-omics profiling-derived signature links cellular ecosystem to glioblastoma prognosis

doi: 10.1016/j.isci.2026.115982

Figure Lengend Snippet: Spatial transcriptomic analysis revealing co-localization of ECMSig, hypoxia, Scissor-Positive cells, and pericytes in GBM (A) Spatial feature plots for four GBM samples. Each row represents a sample. Columns show spatial heatmaps of: ECMSig score, hypoxia signature score, tumor Scissor_Pos signature score, myeloid Scissor_Pos signature score, endothelial Scissor Pos signature score, and pericyte marker signature score. Color scale indicates scaled expression or score (low to high). Each dot represents a spatial barcoded spot.

Article Snippet: The single-cell transcriptomic sequencing dataset utilizing technology from the 10X Genomics platform was available under the accession number GEO: GSE182109 at the Gene Expression Omnibus (GEO) repository.

Techniques: Marker, Expressing

(A) Heatmap of DEG cluster analysis for DT and CK transcriptomes. The black dendrogram shows hierarchical clustering of samples and genes with similar expression patterns. The color scale represents normalized gene expression levels (yellow/orange = high expression, blue = low expression), demonstrating clear separation between CK and DT groups. (B) Volcano plot of differentially expressed genes. Orange dots indicate up-regulated genes (log 2 FC > 1, p < 0.05), blue dots indicate down-regulated genes (log 2 FC < −1, p < 0.05), and gray dots indicate non-significant genes.

Journal: PeerJ

Article Title: A cellular view of drought adaptation in sugarcane: multi-omics integration reveals a quadruple module network linking water regulation, oxidative defense, cell wall remodeling, and cell cycle regulation

doi: 10.7717/peerj.21396

Figure Lengend Snippet: (A) Heatmap of DEG cluster analysis for DT and CK transcriptomes. The black dendrogram shows hierarchical clustering of samples and genes with similar expression patterns. The color scale represents normalized gene expression levels (yellow/orange = high expression, blue = low expression), demonstrating clear separation between CK and DT groups. (B) Volcano plot of differentially expressed genes. Orange dots indicate up-regulated genes (log 2 FC > 1, p < 0.05), blue dots indicate down-regulated genes (log 2 FC < −1, p < 0.05), and gray dots indicate non-significant genes.

Article Snippet: The transcriptome sequences and annotation information of Saccharum spp. suspension cells under drought stress determined in this study have been uploaded to the National Center for Biotechnology Information ( PRJNA1403770 ) for access by relevant researchers.

Techniques: Expressing, Gene Expression

(A) The abscissa indicates the selected DEGs related to drought stress in sugarcane, and the ordinate indicates the log 2 fold change of gene expression measured by RNA-seq (orange bars) and RT-qPCR (blue bars). The column height represents the differential expression multiple, and the smaller the height difference between orange and blue bars, the more consistent the expression trend between the two methods. (B) Correlation analysis of log 2 fold change values from RNA-seq ( x -axis) and RT-qPCR ( y -axis). Each blue square represents one DEG. The linear regression equation and R 2 value ( R 2 = 0.97581) indicate a high positive correlation between the two techniques, verifying the reliability of the transcriptome sequencing data.

Journal: PeerJ

Article Title: A cellular view of drought adaptation in sugarcane: multi-omics integration reveals a quadruple module network linking water regulation, oxidative defense, cell wall remodeling, and cell cycle regulation

doi: 10.7717/peerj.21396

Figure Lengend Snippet: (A) The abscissa indicates the selected DEGs related to drought stress in sugarcane, and the ordinate indicates the log 2 fold change of gene expression measured by RNA-seq (orange bars) and RT-qPCR (blue bars). The column height represents the differential expression multiple, and the smaller the height difference between orange and blue bars, the more consistent the expression trend between the two methods. (B) Correlation analysis of log 2 fold change values from RNA-seq ( x -axis) and RT-qPCR ( y -axis). Each blue square represents one DEG. The linear regression equation and R 2 value ( R 2 = 0.97581) indicate a high positive correlation between the two techniques, verifying the reliability of the transcriptome sequencing data.

Article Snippet: The transcriptome sequences and annotation information of Saccharum spp. suspension cells under drought stress determined in this study have been uploaded to the National Center for Biotechnology Information ( PRJNA1403770 ) for access by relevant researchers.

Techniques: Gene Expression, RNA Sequencing, Quantitative RT-PCR, Quantitative Proteomics, Expressing, Sequencing

Transcriptomic profiling of the pretreatment biopsy sample. RNA-seq was used to obtain gene expression profiles of tumor samples. A global embedding of cancer types was calculated by transforming these gene expression profiles using PCA, followed by a t-SNE transformation to obtain a 2-dimensional approximate “map” of transcriptional similarity. The new patient sample is projected onto a pre-calculated PCA embedding space based on historical samples analyzed at Caris. Its proximity to other samples on the map reflects shared transcriptional programs with these samples. Only the immediate neighborhoods of samples should be considered for further interpretation since t-SNE embeddings do not preserve similarity correlations over long distances. Each point on the t-SNE map represents a different sample. The target symbol represents the present patient’s pretreatment biopsy sample. PCA, principal component analysis; t-SNE, t-stochastic neighbor embedding.

Journal: Therapeutic Advances in Medical Oncology

Article Title: Lineage infidelity in FH-deficient RCC with secondary somatic alterations: a case report and implications for diagnosis and treatment

doi: 10.1177/17588359261456676

Figure Lengend Snippet: Transcriptomic profiling of the pretreatment biopsy sample. RNA-seq was used to obtain gene expression profiles of tumor samples. A global embedding of cancer types was calculated by transforming these gene expression profiles using PCA, followed by a t-SNE transformation to obtain a 2-dimensional approximate “map” of transcriptional similarity. The new patient sample is projected onto a pre-calculated PCA embedding space based on historical samples analyzed at Caris. Its proximity to other samples on the map reflects shared transcriptional programs with these samples. Only the immediate neighborhoods of samples should be considered for further interpretation since t-SNE embeddings do not preserve similarity correlations over long distances. Each point on the t-SNE map represents a different sample. The target symbol represents the present patient’s pretreatment biopsy sample. PCA, principal component analysis; t-SNE, t-stochastic neighbor embedding.

Article Snippet: To confirm the diagnosis, molecular testing (next-generation sequencing (NGS); whole exome and whole transcriptome sequencing, Caris Life Sciences (Irving, Texas, United States)) revealed an S419P mutation in the FH gene, along with pathogenic mutations in KMT2A , NF2 , and TP53 , discussed in greater detail below.

Techniques: RNA Sequencing, Gene Expression, Transformation Assay