Identification of key pathways and candidate genes in pancreatic ductal adenocarcinoma using bioinformatics analysis

Single-cell analysis of patient-derived PDAC organoids reveals cell state heterogeneity and a conserved developmental hierarchy

Pancreatic ductal adenocarcinoma (PDAC) is projected to be the second leading cause of cancer mortality by 2030. Bulk transcriptomic analyses have distinguished 'classical' from 'basal-like' tumors with more aggressive clinical behavior. We derive PDAC organoids from 18 primary tumors and two matched liver metastases, and show that 'classical' and 'basal-like' cells coexist in individual organoids. By single-cell transcriptome analysis of PDAC organoids and primary PDAC, we identify distinct tumor cell states shared across patients, including a cycling progenitor cell state and a differentiated secretory state. Cell states are connected by a differentiation hierarchy, with 'classical' cells concentrated at the endpoint. In an imaging-based drug screen, expression of 'classical' subtype genes correlates with better drug response. Our results thus uncover a functional hierarchy of PDAC cell states linked to transcriptional tumor subtypes, and support the use of PDAC organoids as a clinically relevant model for in vitro studies of tumor heterogeneity.

Identification of Key Gene Targets for Sensitizing Colorectal Cancer to Chemoradiation: an Integrative Network Analysis on Multiple Transcriptomics Data

PURPOSE

Colorectal cancer (CRC) is a main cause of morbidity and mortality in the world. Chemoradioresistance is a major problem in CRC treatment. Identification of novel therapeutic targets in order to overcome treatment resistance in CRC is necessary.

METHODS

In this study, gene expression omnibus (GEO) database was searched to find microarray datasets. Data normalization/analyzing was performed using ExAtlas. The gene ontology (GO) and pathway enrichment analysis was performed using g:Profiler. Protein-protein interaction network (PPIN) was constructed by Search Tool for the Retrieval of Interacting Genes (STRING) and analyzed using Cytoscape. Survival analysis was done using Kaplan-Meier curve method.

RESULTS

Forty-one eligible datasets were included in study. A total of 12,244 differentially expressed genes (DEGs) and 7337 unique DEGs were identified. Among them, 1187 DEGs were overlapped in ≥ 3 datasets. Fifty-five overlapped genes were considered as hub genes. Common hub genes in chemo/radiation/chemoradiation datasets were chosen as the essential candidate genes (n = 13). Forty-one hub gene and 7 essential candidate genes were contributed in the significant modules. The modules were mainly enriched in the signaling pathways of senescence, autophagy, NF-κB, HIF-1, stem cell pluripotency, notch, neovascularization, cell cycle, p53, chemokine, and PI3K-Akt. NGFR, FGF2, and PROM1 genes were significantly predictors of CRC patient's survival.

CONCLUSION

Our study revealed three-gene signatures as potential therapeutic targets and also candidate molecular markers in CRC chemoradioresistance.

Positive Crosstalk Between Hedgehog and NF-κB Pathways Is Dependent on KRAS Mutation in Pancreatic Ductal Adenocarcinoma

It has been shown that aberrant activation of the Hedgehog (Hh) and nuclear factor-kappa B (NF-κB) signaling pathways plays an important role in the pancreatic carcinogenesis, and KRAS mutation is a hallmark of pancreatic ductal adenocarcinoma (PDAC). Until now, the role of KRAS mutation in the context of crosstalk between Hh and NF-κB signaling pathways in PDAC has not been investigated. This study was to determine whether the crosstalk between the Hh and NF-κB pathways is dependent on KRAS mutation in PDAC. The correlation between Gli1, Shh, NF-κB p65 expression and KRAS mutation in PDAC tissues was firstly examined by immunohistochemistry. Next, Western blotting, qPCR, and immunofluorescence were conducted to examine the biological effects of interleukin-1β (IL-1β) and tumor necrosis factor-alpha (TNF-α) as NF-κB signaling agonists, Shh as an Hh ligand alone or in combination with KRAS small interfering RNA (si-KRAS) in KRAS-mutant PDAC cells (MT-KRAS; SW1990 and Panc-1), wild-type KRAS PDAC cells (WT-KRAS; BxPC-3) and mutant KRAS knock-in BxPC-3 cells in vitro as well as tumor growth in vivo. KRAS mutation-dependent crosstalk between Hh and NF-κB in PDAC cells was further assessed by Ras activity and luciferase reporter assays. The aberrant Hh and NF-κB pathway activation was found in PDAC tissues with KRAS mutation. The same findings were confirmed in MT-KRAS PDAC cells and MT-KRAS knock-in BxPC-3 cells, whereas this activation was not observed in WT-KRAS PDAC cells. However, the activation was significantly down-regulated by KRAS silencing in MT-KRAS PDAC cells. Furthermore, MT-KRAS cancer cell proliferation and survival in vitro and tumor growth after inoculation with MT-KRAS cells in vivo were promoted by NF-κB and Hh signaling activation. The pivotal factor for co-activation of NF-κB and Hh signaling is MT-KRAS protein upregulation, showing that positive crosstalk between Hh and NF-κB pathways is dependent upon KRAS mutation in PDAC.

Gene Expression Network Analysis of Precursor Lesions in Familial Pancreatic Cancer

Purpose: High-grade pancreatic intraepithelial neoplasia (PanIN) are aggressive premalignant lesions, associated with risk of progression to pancreatic ductal adenocarcinoma (PDAC). A depiction of co-dysregulated gene activity in high-grade familial pancreatic cancer (FPC)-related PanIN lesions may characterize the molecular events during the progression from familial PanIN to PDAC. Materials and Methods: We performed weighted gene coexpression network analysis (WGCNA) to identify clusters of coexpressed genes associated with FPC-related PanIN lesions in 13 samples with PanIN-2/3 from FPC predisposed individuals, 6 samples with PDAC from sporadic pancreatic cancer (SPC) patients, and 4 samples of normal donor pancreatic tissue. Results: WGCNA identified seven differentially expressed gene (DEG) modules and two commonly expressed gene (CEG) modules with significant enrichment for Gene Ontology (GO) terms in FPC and SPC, including three upregulated (p < 5e-05) and four downregulated (p < 6e-04) gene modules in FPC compared to SPC. Among the DEG modules, the upregulated modules include 14 significant genes (p < 1e-06): ALOX12-AS1, BCL2L11, EHD4, C4B, BTN3A3, NDUFA11, RBM4B, MYOC, ZBTB47, TTTY15, NAPRT, LOC102606465, LOC100505711, and PTK2. The downregulated modules include 170 genes (p < 1e-06), among them 13 highly significant genes (p < 1e-10): COL10A1, SAMD9, PLPP4, COMP, POSTN, IGHV4-31, THBS2, MMP9, FNDC1, HOPX, TMEM200A, INHBA, and SULF1. The DEG modules are enriched for GO terms related to mitochondrial structure and adenosine triphosphate metabolic processes, extracellular structure and binding properties, humoral and complement mediated immune response, ligand-gated ion channel activity, and transmembrane receptor activity. Among the CEG modules, IL22RA1, DPEP1, and BCAT1 were found as highly connective hub genes associated with both FPC and SPC. Conclusion: FPC-related PanIN lesions exhibit a common molecular basis with SPC as shown by gene network activities and commonly expressed high-connectivity hub genes. The differential molecular pathology of FPC and SPC involves multiple coexpressed gene clusters enriched for GO terms including extracellular activities and mitochondrion function.