A graphic method for identification of novel glioma related genes

Identification of potential pathogenic biomarkers in clear cell renal cell carcinoma

The purpose of the present study was to screen potential pathogenic biomarkers of clear cell renal cell carcinoma (ccRCC) via microarray analysis. The mRNA and microRNA (miRNA) expression profiles of GSE96574 and GSE71302 were downloaded from the Gene Expression Omnibus (GEO) database, as well as the methylation profile of GSE61441. A total of 5 ccRCC tissue samples and 5 normal kidney tissue samples were contained in each profile of GSE96574 and GSE71302, and 46 ccRCC tissue samples and 46 normal kidney tissue samples were involved in GSE61441. The differentially expressed genes (DEGs) and the differentially expressed miRNAs (DEMs) were obtained via limma package in ccRCC tissues compared with normal kidney tissues. The Two Sample t-test and the Beta distribution test were used to identify the differentially methylated sites (DMSs). The Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to perform the Gene Ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs. The targets of the DEMs were screened with the miRWalk database, and the further combination analyses of DEGs, DEMs and DMSs were conducted. Additionally, reverse transcription PCR (RT-PCR) and methylation-specific PCR (MS-PCR) were performed to detect the mRNA level and methylation status of HAPLN1. The mRNA levels of hsa-miR-204 and hsa-miR-218 were tested by RT-PCR. A total of 2,172 DEGs, 202 DEMs and 2,172 DMSs were identified in RCC samples compared with normal samples. The DEGs were enriched in 1,015 GO terms and 69 KEGG pathways. A total of 10,601 miRNA-gene pairs were identified in at least 5 algorithms of the miRWalk database. A total of 143 overlaps were identified between the DEGs and the differentially methylated genes. Furthermore, the DEGs were involved in 851 miRNA-gene pairs, including 127 pairs in which the target genes were negatively associated with their corresponding DEMs and DMSs. HAPLN1 was lowly expressed and highly methylated in ccRCC tissues, while hsa-miR-204 and hsa-miR-218 were highly expressed. The results of the present study indicated that HAPLN1, hsa-miR-204 and hsa-miR-218 may be involved in the pathogenesis of ccRCC.

An integrated method for the identification of novel genes related to oral cancer

Cancer is a significant public health problem worldwide. Complete identification of genes related to one type of cancer facilitates earlier diagnosis and effective treatments. In this study, two widely used algorithms, the random walk with restart algorithm and the shortest path algorithm, were adopted to construct two parameterized computational methods, namely, an RWR-based method and an SP-based method; based on these methods, an integrated method was constructed for identifying novel disease genes. To validate the utility of the integrated method, data for oral cancer were used, on which the RWR-based and SP-based methods were trained, thereby building two optimal methods. The integrated method combining these optimal methods was further adopted to identify the novel genes of oral cancer. As a result, 85 novel genes were inferred, among which eleven genes (e.g., MYD88, FGFR2, NF-κBIA) were identified by both the RWR-based and SP-based methods, 70 genes (e.g., BMP4, IFNG, KITLG) were discovered only by the RWR-based method and four genes (L1R1, MCM6, NOG and CXCR3) were predicted only by the SP-based method. Extensive analyses indicate that several novel genes have strong associations with cancers, indicating the effectiveness of the integrated method for identifying disease genes.

Elevated expression levels of serum insulin-like growth factor-1, tumor necrosis factor-α and vascular endothelial growth factor 165 might exacerbate type 2 diabetic nephropathy

AIMS/INTRODUCTION

The present study aimed to determine the associations between expressions of insulin-like growth factor-1 (IGF-1), tumor necrosis factor-α (TNF-α) and vascular endothelial growth factor 165 (VEGF₁₆₅ ) in serum, and occurrence and development of type 2 diabetic nephropathy (DN).

MATERIALS AND METHODS

A total of 108 patients diagnosed as DN were randomly selected, including 50 patients in the microalbuminuria group, 44 patients in the macroalbuminuria group and 14 patients in the renal insufficiency group. Meanwhile, 97 healthy people were collected as a normal control group. Urinary albumin (UALB) and urine creatinine (Cr) of all participants were measured for 24 h, with their ratio (UALB/Cr) being calculated. Enzyme-linked immunosorbent assay was used to detect the serum concentrations of IGF-1, TNF-α and VEGF₁₆₅ .

RESULTS

The expressions of serum IGF-1, TNF-α and VEGF₁₆₅ in the type 2 DN patients were significantly higher than those in the control group (all P < 0.05). The expressions of serum IGF-1, TNF-α and VEGF₁₆₅ in the type 2 DN patients were positively correlated with UALB/Cr (all P < 0.05). As type 2 DN worsened, the expressions of serum IGF-1, TNF-α and VEGF₁₆₅ increased, and type 2 DN severity had positive correlations with serum IGF-1, TNF-α and VEGF₁₆₅ concentrations (all P < 0.05). There was a positive association between IGF-1 and TNF-α, IGF-1 and VEGF₁₆₅ , and TNF-α and VEGF₁₆₅ (all P < 0.05). Logistic regression analysis showed that IGF-1 and VEGF₁₆₅ were associated with the progression of DN (both P < 0.05).

CONCLUSIONS

Elevated expression levels of serum IGF-1, TNF-α and VEGF₁₆₅ might exacerbate type 2 DN.

The Use of Protein-Protein Interactions for the Analysis of the Associations between PM2.5 and Some Diseases

Nowadays, pollution levels are rapidly increasing all over the world. One of the most important pollutants is PM2.5. It is known that the pollution environment may cause several problems, such as greenhouse effect and acid rain. Among them, the most important problem is that pollutants can induce a number of serious diseases. Some studies have reported that PM2.5 is an important etiologic factor for lung cancer. In this study, we extensively investigate the associations between PM2.5 and 22 disease classes recommended by Goh et al., such as respiratory diseases, cardiovascular diseases, and gastrointestinal diseases. The protein-protein interactions were used to measure the linkage between disease genes and genes that have been reported to be modulated by PM2.5. The results suggest that some diseases, such as diseases related to ear, nose, and throat and gastrointestinal, nutritional, renal, and cardiovascular diseases, are influenced by PM2.5 and some evidences were provided to confirm our results. For example, a total of 18 genes related to cardiovascular diseases are identified to be closely related to PM2.5, and cardiovascular disease relevant gene DSP is significantly related to PM2.5 gene JUP.

Identification of novel thyroid cancer-related genes and chemicals using shortest path algorithm

Thyroid cancer is a typical endocrine malignancy. In the past three decades, the continued growth of its incidence has made it urgent to design effective treatments to treat this disease. To this end, it is necessary to uncover the mechanism underlying this disease. Identification of thyroid cancer-related genes and chemicals is helpful to understand the mechanism of thyroid cancer. In this study, we generalized some previous methods to discover both disease genes and chemicals. The method was based on shortest path algorithm and applied to discover novel thyroid cancer-related genes and chemicals. The analysis of the final obtained genes and chemicals suggests that some of them are crucial to the formation and development of thyroid cancer. It is indicated that the proposed method is effective for the discovery of novel disease genes and chemicals.