Translational studies on pancreatic cancer and gastric cancer: a methodology in PhD thesis

Abstract

Pancreatic ductal adenocarcinoma (PDAC) and gastric adenocarcinoma (GA) are aggressive cancers with poor prognoses, demanding innovative approaches to advance treatment strategies and prevention efforts. This article presents a methodology in connection with PhD thesis on PDAC and GA, including motivation and knowledge in literature (Paper I), various research models (Paper II), knowledge discovery (Papers III and IV), and thesis assessment and evaluation (dissertation). The four studies aimed to address the gaps between patients and researchers and between basic and clinical research. Patient and Public Involvement (PPI) was explored to align research priorities with patients' needs. While PPI emphasized the importance of treatment-focused research, researchers and scientific journals prioritized basic science. Research guidance of "Findable, Accessible, Interoperable, and Reusable" (FAIR) was implanted in the studies, particularly proteomics datasets of different research models on PDAC. An analytic workflow for knowledge discovery with systems modeling was developed, leading to identification of translational targets of proteins and signaling networks on PDAC. Gastric intestinal metaplasia (GIM) is associated with GA. Multi-bioinformatics identified potential biomarkers for GA-related GIM, including genes and signaling networks. Potential repurposed drugs were also identified for both PDAC and GIM. In conclusion, the methodology was instructive in completing PhD thesis, whereas the findings in the original papers added new knowledge in translational research on PDAC and GA.

Keywords: PhD research methodology; cancer; knowledge discovery; patient and public involvement; proteomics.

FIGURE 1

Classification, treatment strategies, and prognosis for pancreatic cancer. The disease can be categorized as resectable, borderline resectable, locally advanced, or metastatic, with corresponding survival rates. Resectable and borderline cases are treated with surgery and chemotherapy, offering a median survival of 20–24 months and a 5-year survival rate of ∼20%. Locally advanced and metastatic cases rely on palliative chemotherapy, with median survival of 9–12 months and 5–9 months, respectively, and no significant 5-year survival (Network, 2024). Created in Ilustrator.

FIGURE 2

Key risk factors and molecular events in the progression of gastric cancer (GC). Risk factors include dietary intake, smoking, alcohol, bile acids, H. pylori (CagA, VacA), EBV, and genetic predispositions (e.g., CDH1, TP53 mutations, inflammation-related polymorphisms). The so-called Correa’s cascade progresses from normal mucosa to gastritis, atrophic gastritis, intestinal metaplasia, dysplasia, and GC. Each stage features distinct molecular changes. Created in Illustrator.

FIGURE 3

Thesis design. This thesis integrates patient and public involvement (PPI) to align research priorities with end-user perspectives. Paper I explores end-user interests in pancreatic cancer research, compared to researchers’ priorities. Paper II presents proteomic data from six preclinical models of pancreatic cancer, validated for relevance and made publicly accessible to guide model selection. Paper III applies systems biology to compare these models with patient data, identifying hub proteins and key pathways. Paper IV focuses on transcriptomic data from GIM and GA patients to identify biomarkers and explore drug repurposing opportunities for gastric cancer prevention. Created in BioRender.

FIGURE 4

Graphic summary of PPI results of Paper I. Of note, gaps were identified from questionnaire and literature analysis.

FIGURE 5

Graphic summary of FAIR results of Paper II. Of note, eight datasets of proteomics are in FAIR.

FIGURE 6

Graphic summary of systems modeling of Paper III. Of note, matched proteins between PDAC research models and patients were identified for further biomarkers and drug targets (including drug repurposing).

FIGURE 7

Graphic summary of multi-omics for discovery of biomarkers of Paper IV. Of note, multi-bioinformatics was applied to identify potential biomarkers and drug development.