Isolation and bioinformatics analysis of differentially methylated genomic fragments in human gastric cancer

Genome-wide DNA methylation and RNA expression profiles identified RIPK3 as a differentially methylated gene in Chlamydia pneumoniae infection lung carcinoma patients in China

Aim: To explore the relationship between Chlamydia pneumonia (Cpn) infection and lung cancer using integrative methylome and transcriptome analyses. Methods: Twelve primary lung cancer patients who were positive for Cpn and twelve patients who were negative were selected for demographic, clinicopathological, and lifestyle matching. Genomic DNA and RNA were extracted and DNA methylation and mRNA levels were detected using the Infinium Human Methylation 450 Beadchip array and mRNA + lncRNA Human Gene Expression Microarray. We identified differentially expressed methylation and genes profiles. Results: Integrative analysis revealed an inverse correlation between differentially expressed genes and DNA methylation. Cpn-related lung cancer methylated genes (target genes) were introduced into the gene ontology and KEGG, PID, BioCarta, Reactome, BioCyc and PANTHER enrichment analyses using a q-value cutoff of 0.05 to identify potentially functional methylation of abnormal genes associated with Cpn infection. Gene sets enrichment analysis was evaluated according to MsigDB. Levels of differentially expressed methylated sites were quantitatively verified. The promoter methylation sites of 62 genes were inversely related to expression levels. According to the quantitative analysis of DNA methylation, the methylation level of the RIPK3 promoter region was significantly different between Cpn-positive cancerous and adjacent tissues, but not between Cpn-negative cancerous and adjacent tissues. Conclusion:  Hypomethylation of the RIPK3 promoter region increases RIPK3 expression, leading to regulated programmed necrosis and activation of NF-κB transcription factors, which may contribute to the development and progression of Cpn-related lung cancer.

Differential gene expression of the key signalling pathway in para-carcinoma, carcinoma and relapse human pancreatic cancer

Pancreatic cancer (PC) has a high rate of mortality and a poorly understood mechanism of progression. Investigation of the molecular mechanism of PC and exploration of the specific markers for early diagnosis and specific targets of therapy are key points to prevent and treat PC effectively and to improve their prognosis. In our study, expression profiles experiment of para-carcinoma, carcinoma and relapse human PC was performed using Agilent human whole genomic oligonucleotide microarrays with 45 000 probes. Differentially expressed genes related with PC were screened and analysed further by Gene Ontology term analysis and Kyoto encyclopaedia of genes and genomes pathway analysis. Our results showed that there were 3853 differentially expressed genes associated with pancreatic carcinogenesis and relapse. In addition, our study found that PC was related to the Jak-STAT signalling pathway, PPAR signalling pathway and Calcium signalling pathway, indicating their potential roles in pancreatic carcinogenesis and progress.

Diagnostic model of saliva protein finger print analysis of patients with gastric cancer

AIM: To explore the method for early diagnosis of gastric cancer by screening the expression spectrum of saliva protein in gastric cancer patients using mass spectrometry for proteomics.

METHODS: Proportional peptide mass fingerprints were obtained by analysis based on proteomics matrix-assisted laser desorption ionization time-of-flight/mass spectrometry. A diagnosis model was established using weak cation exchange magnetic beads to test saliva specimens from gastric cancer patients and healthy subjects.

RESULTS: Significant differences were observed in the mass to charge ratio (m/z) peaks of four proteins (1472.78 Da, 2936.49 Da, 6556.81 Da and 7081.17 Da) between gastric cancer patients and healthy subjects.

CONCLUSION: The finger print mass spectrum of saliva protein in patients with gastric cancer can be established using gastric cancer proteomics. A diagnostic model for distinguishing protein expression mass spectra of gastric cancer from non-gastric-cancer saliva can be established according to the different expression of proteins 1472.78 Da, 2936.49 Da, 6556.81 Da and 7081.17 Da. The method for early diagnosis of gastric cancer is of certain value for screening special biological markers.