Over the past three decades, pancreatic cancer (PC) has made few breakthroughs in practice compared to other cancers. With the development of next-generation sequencing (NGS) technology, RNA sequencing (RNA-seq) provides a powerful tool for cancer research by defining the transcriptional composition of cancer and related cells, enabling the detection of splicing isoforms and somatic mutations with high sensitivity. Moreover, RNA-Seq has the capability to capture both coding and non-coding RNAs and provide strand orientation for a complete view of expression dynamics. Thanks to our established large-scale sequencing platforms, Alfa Oncology can provide flexible and customized PC RNA-seq and bioinformatics analysis services to customers worldwide. Our seasoned scientists can provide the most accurate detection and quantification of RNA in PC.
Overview of cancer RNA-Seq
RNA-Seq can help researchers understand the classification and progression of cancer by monitoring gene expression and transcriptome changes. Cancer accumulates many genetic alterations, but usually, only a few lead to tumor progression. With the help of RNA-seq, researchers can detect splicing isoforms and somatic mutations, discover new small RNAs that regulate gene expression, identify gene fusions caused by chromosomal translocations, identify gene expression signatures, and mutational profiles, and so on. RNA-seq method has been performed in PC research with cell lines, circulating tumor cells, and PC tissues. The discovery of new differentially expressed genes and canonical pathways by this sequencing technology has provided important clues for understanding the molecular mechanism of PC pathogenesis and opened up a new area of research in PC.
The service offering at Alfa Oncology
Alfa Oncology is now offering PC RNA-seq services. Our services mainly consist of five parts, including experimental design, sample preparation, RNA-Seq, bioinformatics analysis, and customized analysis. Our services can facilitate the identification of PC therapeutic targets, the discovery of PC-specific biomarkers, and the investigation of a range of pathways involved in PC progression.
Workflow of our service:
Our customized analysis:
- Differential gene expression (DE) analysis
DE analysis is performed to investigate differentially expressed genes (DEGs) when comparing the tumor and adjacent benign pancreatic tissues in PC, or when comparing PC cells with normal cells.
- Gene co-expression network analysis
This analysis is performed to identify module co-expressed genes, which are closely related to PC, and the identification of central genes in the modules.
- Gene set enrichment analysis
Identify functionally relevant genes involved in different pathways and regulating the expression of other genes, with a focus on highly expressed genes and underexpressed genes. Further exploration of these important pathways can be implemented for the therapeutic targeting of PaCa.
- The Cancer Genome Atlas (TCGA) RNA-seq data analysis
PC gene expression data (RNA-seq) from the TCGA database is applied to validate the reliability of the sequencing results.
- Identification of microRNAs (miRNAs) associated with PC
Exploration of key genes and miRNAs associated with the pathogenesis of PC, involving the identification of DE-miRNAs, target gene prediction, and functional enrichment analysis.
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- Nisar, Maryum, et al. "Integrated analysis of microarray and RNA-seq data for the identification of hub genes and networks involved in the pancreatic cancer." Frontiers in genetics 12 (2021): 626.
- Jaiswal, Alokita, and Imlimaong Aier. "Exploring gene expression levels in Pancreatic Ductal Adenocarcinoma (PDAC) using RNA-Seq data." 2018 International Conference on Bioinformatics and Systems Biology (BSB). IEEE, 2018.