Investigation of Molecular Mechanisms of S-1, Docetaxel and Cisplatin in Gastric Cancer with a History of Helicobacter Pylori Infection.

Gastric cancer rates and fatality rates have not decreased. Gastric cancer treatment has historically included surgery (both endoscopic and open), chemotherapy, targeted therapy, and immunotherapy. One of the aggravating carriers of this cancer is Helicobacter pylori infection. Various drug combinations are used to treat gastric cancer. However, examining the molecular function of these drugs, depending on whether or not there is a history of Helicobacter pylori infection, can be a better help in the treatment of these patients. This study was designed as bioinformatics. Various datasets such as patients with gastric cancer, with and without a history of H. pylori, and chemotherapy drugs cisplatin, docetaxel, and S-1 were selected. Using Venn diagrams, the similarities between gene expression profiles were assessed and isolated. Then, selected the signal pathways, ontology of candidate genes and proteins. Then, in clinical databases, we confirmed the candidate genes and proteins. The association between gastric cancer patients with and without a history of H. pylori with chemotherapy drugs was investigated. The pathways of cellular aging, apoptosis, MAPK, and TGFß were clearly seen. After a closer look at the ontology of genes and the relationship between proteins, we nominated important biomolecules. Accordingly, NCOR1, KIT, MITF, ESF1, ARNT2, TCF7L2, and KRR1 proteins showed an important role in these connections. Finally, NCOR1, KIT, KRR1, and ESF1 proteins showed a more prominent role in the molecular mechanisms of S-1, Docetaxel, and Cisplatin in gastric cancer associated with or without H. pylori.

Investigation of key signaling pathways and appropriate diagnostic biomarkers selection between non-invasive to invasive stages in pancreatic cancer: a computational observation.

Pancreatic cancer is the seventh most lethal cancer in the world. Despite its moderate prevalence, the 5-year survival rate of patients with pancreatic cancer is about 10%. Despite different therapeutic and diagnostic strategies for pancreatic cancer, this cancer is still uncontrollable in the invasive stage and can invade various body organs and cause death. Early detection for pancreatic cancer can be an excellent solution to manage treatment better and increase patients' survival rates. This study aimed to find diagnostic biomarkers between non-invasive to invasive stages of pancreatic cancer in the extracellular matrix to facilitate the early diagnosis of this cancer. Using bioinformatics analysis, we selected the appropriate datasets between non-invasive and invasive pancreatic cancer stages and categorized their genes. Then, we charted and confirmed the signaling pathways, gene ontology, protein relationships, and protein expression levels in the human samples using bioinformatics databases. Cell adhesion and hypoxia signaling pathways were observed in up-regulated genes, different phases of the cell cycle, and metabolic signaling pathways with down-regulated genes between non-invasive and invasive pancreatic cancer stages. For proper diagnostic biomarkers selection, the overexpressed genes that released protein into the extracellular matrix were examined in more detail, with 62 proteins selected and SPARC, THBS2, COL11A1, COL1A1, COL1A2, COL3A1, SERPINH1, PLAU proteins chosen. Bioinformatics analysis can more accurately assess the relationship between molecular mechanisms and key actors in pancreatic cancer invasion and metastasis to facilitate early detection and improve treatment management for patients with pancreatic cancer.