Abstract
Exploring the underlying mechanisms of cancer development is useful for cancer treatment. In this paper, we analyzed the transcriptome profiles from the human normal pancreas, pancreatitis, pancreatic cancer and metastatic pancreatic cancer to study the intricate associations among pancreatic cancer progression. We clustered the transcriptome data, and analyzed the differential expressed genes. WGCNA was applied to construct co-expression networks and detect important modules. Importantly we selected the module in a different way. As the pancreatic disease deteriorates, the number of differentially expressed genes increases. The gene networks of T cells and interferon are upregulated in stages. In conclusion, the network-based study provides gradually activated gene networks in the disease progression of pancreatitis, pancreatic cancer, and metastatic pancreatic cancer. It may contribute to the rational design of anti-cancer drugs.
Highlights
Exploring the underlying mechanisms of cancer development is useful for cancer treatment
If some genes are gradually upregulated among the normal pancreas, pancreatitis, pancreatic cancer and metastatic pancreatic cancer, they are likely to be classified in the same module
Gene expression profiling data E-EMBL-6 contains the stages of the normal state, chronic pancreatitis, pancreatic cancer, and metastatic pancreatic cancer, each with nine samples[18]
Summary
Nine chronic pancreatic tissue samples were obtained from two female and seven male patients (median age 52 years; range 42–62 years). Nine pancreatic cancer tissue samples were obtained from seven male and two female patients (median age 63 years; range 53–77 years). Nine metastatic pancreatic cancer tissue samples were obtained from four female and five male patients (median age 58.5 years; range 58–78 years). Normal human tissue samples were obtained through an organ donor program from nine previously healthy individuals (five male donors, four female donors; median age 55 years; range 21–73 years). All clustering methods included hierarchical clustering, K-Means clustering, and Self-organizing maps Based on these data, significance analysis of microarrays (SAM) was performed to select the inversely regulated genes. This article does not contain any studies with human participants or animals performed by any of the authors
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