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.
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.
Project description:Purposes: To investigate the biological function of tRF in breast cancer by tRF and tiRNA sequencing Methods: Breast cancer tissue samples and matched non-tumor adjacent tissues were obtained from five patients. Small RNA sequencing was performed on Illumina NexSeq instrument Results: If P ≤ 0.05, fold change ≥ 2 as the cut off, there were 3 up-regulated tRFs & tiRNAs and 13 down-regulated tRFs & tiRNAs. Conclusions:There were 3 up-regulated tRFs & tiRNAs and 13 down-regulated tRFs & tiRNAs in breast cancer tissue samples and matching adjacent tissue samples
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.
