Similar Datasets
Project description:RNA-sequencing: HNSCC tissue and adjacent normal tissue
| PRJNA668735 | ENA
Project description:RNA-sequencing for 10 samples
2020-08-19 | GSE136000 | GEO
Project description:Cervical cancer (CC) is the fourth leading cause of deaths in gynecological malignancies. Although the etiology of CC has been extensively investigated, the exact pathogenesis of CC remains incomplete. Recently, single-cell technologies demonstrated advantages in exploring intra-tumoral diversification among various tumor cells. However, single-cell transcriptome (scRNA-seq) analysis of CC cells and microenvironment has not been conducted. In this study, a total of 6 samples (3 CC and 3 adjacent normal tissues) were examined by scRNA-seq. Here, we performed single-cell RNA sequencing (scRNA-seq) to survey the transcriptomes of 57,669 cells derived from three CC tumors with paired normal adjacent non-tumor (NAT) samples. Single-cell transcriptomics analysis revealed extensive heterogeneity in malignant cells of human CCs, wherein epithelial subpopulation exhibited different genomic and transcriptomic signatures. We also identified cancer-associated fibroblasts (CAF) that may promote tumor progression of CC, and further distinguished inflammatory CAF (iCAF) and myofibroblastic CAF (myCAF). CD8+ T cell diversity revealed both proliferative (MKI67+) and non-cycling exhausted (PDCD1+) subpopulations at the end of the trajectory path. We used the epithelial signature genes derived from scRNA-seq to deconvolute bulk RNA-seq data of CC, identifying four different CC subtypes, namely hypoxia (S-H subtype), proliferation (S-P subtype), differentiation (S-D subtype), and immunoactive (S-I subtype) subtype. Our results lay the foundation for precision prognostic and therapeutic stratification of CC.
2022-10-04 | E-MTAB-11948 | biostudies-arrayexpress
Project description:Laryngeal squamous cell carcinoma (LSCC) is a common form of head and neck cancer with poor prognosis. However, the mechanism underlying the pathogenesis of LSCC remains unclear. We performed high throughout sequencing to identify miRNA that associated with LSCC progression.Small RNA libraries were constructed followed by sequencing on a cohort of LSCC tissues (50 samples) and paired adjacent normal mucosa tissues (50 samples).
2024-09-09 | GSE132222 | GEO
Project description:Laryngeal squamous cell carcinoma (LSCC) is a common form of head and neck cancer with poor prognosis. However, the mechanism underlying the pathogenesis of LSCC remains unclear. We performed high throughout sequencing to identify miRNA that associated with LSCC progression.Small RNA libraries were constructed followed by sequencing on a cohort of LSCC tissues (57 samples) and paired adjacent normal mucosa tissues (57 samples).
2020-06-17 | GSE133632 | GEO
Project description:Total RNA was extracted from human gastric cancer tissues (n=4) and matched adjacent normal tissues (n=4) . RNA samples were analyzed by RNA sequencing based on the manufacturer’s protocols. Briefly, Illumina HiSeq 4000 platform was used to sequence the RNA samples for the subsequent generation of raw data. R package was utilized to select lncRNAs with significantly differential expression based on fold change >2 or <1/2, p value <0.05 between human gastric cancer tissues and matched adjacent normal tissues, and the top 10 upregulated lncRNAs were selected for further study.
2022-10-12 | GSE172032 | GEO
Project description:Purpose: The goals of this study are using whole-exome and RNA sequencing technologies on 63 cases with TFE3-tRCC to explore the molecular characteristics of TFE3-tRCC and provide potential effective therapeutic strategies Methods: Total RNA was isolated from each sample (63 tumor samples and 14 paired adjacent normal samples) using Qiagen RNeasy formalin-fixed paraffin-embedded (FFPE) Kit (Qiagen, Hilden, Germany). Strand-specific RNA sequencing libraries were generated using the Whole RNA-seq Lib Prep kit for Illumina (ABclonal, China). Final libraries were sequenced at the Novogene Bioinformatics Institute (Beijing, China) on an Illumina Hiseq X10 platform by a 150bp paired-end reads. The raw RNA-sequencing reads were filtered by FastQC, Reads were aligned using STAR v2.7.0f with default parameters to the Ensembl human genome assembly GRCh37. Results: Using an optimized data analysis workflow, we mapped about 30 million sequence reads per sample to the mouse genome (build mm9) and identified 16,014 transcripts in the retinas of WT and Nrl−/− mice with BWA workflow and 34,115 transcripts with TopHat workflow. RNA-seq data confirmed stable expression of 25 known housekeeping genes, and 12 of these were validated with qRT–PCR. RNA-seq data had a linear relationship with qRT–PCR for more than four orders of magnitude and goodness of fit (R2) of 0.8798. Approximately 10% of the transcripts showed differential expression between the WT and Nrl−/− retina, with a fold change ≥1.5 and p value <0.05. Altered expression of 25 genes was confirmed with qRT–PCR, demonstrating the high degree of sensitivity of the RNA-seq method. Hierarchical clustering of differentially expressed genes uncovered several as yet uncharacterized genes that may contribute to retinal function. Data analysis with BWA and TopHat workflows revealed a significant overlap yet provided complementary insights in transcriptome profiling. Conclusions: Our study represents the first detailed analysis of retinal transcriptomes, with biologic replicates, generated by RNA-seq technology. The optimized data analysis workflows reported here should provide a framework for comparative investigations of expression profiles. Our results show that NGS offers a comprehensive and more accurate quantitative and qualitative evaluation of mRNA content within a cell or tissue. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions.
2021-07-24 | GSE167573 | GEO
Project description:Purpose: Liver-specific MPC-knockout (Mpc1-/-, LivKO) mice develop less liver tumors than wild-type (Mpc1+/+) mice when given a DEN/CCl4 hepatocarcinogenesis protocol. The goals of this study are to compare transcriptome changes (RNA-seq) between liver tumor and normal-adjacent tissue in WT and Mpc1-/- mice. Methods: Total RNA was collected from tumor and paired normal-adjacent liver samples using the Qiagen miRNeasy kit. RNA from four samples each of wild-type tumor (WT-Tumor), paired wild-type normal-adjacent (WT normal adjacent), Mpc1-/- (MPC LivKO) tumor (MPC LivKO-Tumor), and paired MPC LivKO normal-adjacent (MPC LivKO normal adjacent) tissue was isolated. Each tumor and its paired normal-adjacent tissue were analyzed in a paired manner. Library preparation and sequencing were performed using the Illumina mRNA-Seq workflow. For data normalization, the raw number of reads for each transcript was converted to Fragments Per Kilobase of transcript per Million mapped reads (FPKM). FPKM values were log transformed, and unsupervised clustering was performed on samples based on normalized expression of genes with variation in Euclidean distance among samples of at least 2.5 standard deviations using Cluster 3 software. Results: Using an optimized data analysis workflow, we mapped about 50 million sequence reads per sample to the mouse genome (buildmm10) and identified 15,777 transcripts in the liver tissue samples of WT an Mpc1-/- (MPC LivKO) with Illumina workflow. FPKM values were log transformed, and unsupervised clustering was performed using Cluster 3 software. Unsupervised clustering analysis identified six gene expression groups: (1) increased gene expression in both WT and LivKO tumors, (2) increased gene expression in WT tumors, (3) increased gene expression in MPC LivKO tumors, (4) decreased gene expression down in both WT and LivKO tumors, (5) decreased gene expression in WT tumors, and (6) decreased gene expression in MPC LivKO tumors. Conclusions: Our study is the first on Mpc1-/- liver tumors. The HCC markers Alpha-fetoprotein (Afp) and Glypican-3 (Gpc3) were in the cluster of genes upregulated in both WT and MPC LivKO tumors. In the cluster of 14 genes up-regulated in only WT tumors were two GSTs: Gsta1 and Gstp2. In the cluster of 108 genes down-regulated in only MPC LivKO tumors were three GSTs: Gsta2, Gsta3, and Mgst1. That same cluster contained Gpx1, glutathione peroxidase (Gpx1). Thus, we concluded WT tumors increased but MPC LivKO tumors decreased expression of glutathione metabolizing genes.
2019-09-03 | GSE132728 | GEO
Project description:RNA-sequencing of glioma and adjacent tissue
| PRJNA560920 | ENA
Project description:We selected humann intervertebral disc samples to perform proteomics analysis. There were 1 case of grade I , 1 case of grade II, 3 cases of grade Ⅲ and 3 cases of grade Ⅳ according to Pfirrmann classfication. RNA seqencing analysis and single-cell RNA sequencing were integrated with proteomics data to identify the hub genes for intervertebral disc degeneration using bioinformatic method.
2023-05-10 | PXD040593 | Pride