Identification of potential immune-related prognostic biomarkers of lung cancer using gene co-expression network analysis

Objective The objective of this study was to identify new carcinogenetic hub genes and develop the integration of differentially expressed genes to predict the prognosis of lung cancer.Methods GSE139032 microarray data packages were downloaded from the Gene Expression Omnibus for planning,testing,and review of data.We identified KRT6C,LAMC2,LAMB3,KRT6A,and MYEOV from a key module for validation.Results We found that the five genes were related to a poor prognosis,and the expression levels of these genes were associated with tumor stage.Furthermore,Kaplan-Meier plotter showed that the five hub genes had better prognostic values.The mean levels of methylation in lung adenocarcinoma(LUAD)were significantly lower than those in healthy lung tissues for the hub genes.However,gene set enrichment analysis(GSEA)for single hub genes showed that all of them were immune-related.Conclusion Our findings demonstrated that KRT6C,LAMC2,LAMB3,KRT6A,and MYEOV are all candidate diagnostic and prognostic biomarkers for LUAD.They may have clinical implications in LUAD patients not only for the improvement of risk stratification but also for therapeutic decisions and prognosis prediction.

Identification and Cloning of Differentially Expressed Genes Involved in the Interaction Between Potato and Phytophthora infestans using a Subtractive Hybridization and cDNA-AFLP Combinational Approach

Using a subtractive hybridization （SH）/cDNA-AFLP combinational approach, differentially expressed genes involved in the potato-Phytophthora infestans interaction were identified. These included genes potentially controlling pathogenesis or avr genes in P. infestans as well as those potentially involved in potato resistance or susceptibility to this pathogen. Forty-one differentially expressed transcript, derived fragments （TDFs）, resulting from the interaction, were cloned and sequenced. Two TDFs, suggested as potential pathogenicity factors, have sequence similarity to N-succinyl diaminopimelate aminotransferase and a transcriptional regulator, TetR family gene, respectively. Two other TDFs, suggested as potential avr genes, have sequence similarity to an EST sequence from Avr41Cf.41Avr91Cf- 9 and a P. infestans avirulence-associated gene, respectively. Genes＇ expression and origin were confirmed using Southern blots, Northern blots and qRT-PCR, he., potential resistance gene DL81 was induced at 12 hpi in the moderately resistant cultivar, whereas it was down-regulated as early as 6 hpi in the susceptible cultivar. On the other hand, DL21 was induced at 6 hpi （3.38-fold） in response to the highly aggressive isolate （US8） and strongly up-regulated thereafter （25.13-fold at 120 hpi.）, whereas it was only slightly up-regulated in response to the weakly aggressive isolate US11 （3.82-fold at 96 hpi）, suggesting its potential involvement as a susceptibility gene.

Constructing protein-protein interaction network of hypertension with blood stasis syndrome via digital gene expression sequencing and database mining

OBJECTIVE： To construct a protein-protein interaction（PPI） network in hypertension patients with blood-stasis syndrome（BSS） by using digital gene expression（DGE） sequencing and database mining techniques.METHODS： DGE analysis based on the Solexa Genome Analyzer platform was performed on vascular endothelial cells incubated with serum of hypertension patients with BSS. The differentially expressed genes were f iltered by comparing the expression levels between the different experimental groups. Then functional categories and e nriched pathways of the unique genes for BSS were analyzed using Database for Annotation, Visualization and Integrated Discovery（DAVID） to select those in the enrichment pathways. I nterologous Interaction Database（I2D） was used to construct PPI networks with the selected genes for hypertension patients with BSS. The potential candidate genes related to BSS were identif ied by comparing the number of relationships among genes. Confi rmed by quantitative reverse transcription-polymerase chain reaction（q RTPCR）, gene ontology（GO） analysis was used to infer the functional annotations of the potential candidate genes for BSS.RESULTS： With gene enrichment analysis using DAVID, a list of 58 genes was chosen from the unique genes. The selected 58 genes were analyzed using I2 D, and a PPI network was constructed. Based on the network analysis results, candidate genes for BSS were identifi ed：DDIT3, JUN, HSPA8, NFIL3, HSPA5, HIST2H2 BE, H3F3 B, CEBPB, SAT1 and GADD45 A. Verif ied through qRT-PCR and analyzed by GO, the functional annotations of the potential candidate genes were explored.CONCLUSION： Compared with previous methodologies reported in the literature, the present DGE analysis and data mining method have shown a great improvement in analyzing BSS.