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Breast cancer (BC) is the most common type of malignant tumor and the most common cause of cancer-related mortality among women. Metabolic reprogramming is considered a hallmark of cancer, and the study of BC metabolism may be the key to the development of new strategies for diagnosis and treatment. In this study, we aimed to explore the potential metabolites and gene biomarkers for BC through the integration of metabolomics and transcriptomic data, which could further understand BC tumor biology. Transcriptome dataset GSE139038 was downloaded to explore the differentially expressed genes (DEGs) between BC and normal control (NC) samples. Metabolomics dataset MTBLS326 was downloaded and preprocessed to obtain altered metabolites. Then, the principal component analysis (PCA) and linear models were used to reveal DEGs–metabolites relations. Finally, the pathway enrichment analysis of altered metabolites was performed. A total of 280 DEGs and eight metabolites were explored between BC and NC samples. The liner module analysis investigated 28 DEGs–metabolites interactions including WASP family member 3 (WASF3)–lactate, ras-related protein Rab-7B (RAB7B)–lactate, and methyltransferase-like 7A (METTL7A)–pyruvate. Finally, pathways analysis showed that these metabolites (such as lactate and pyruvate) were mainly enriched in pathways like disorders of the Krebs cycle. Combining with the transcriptomic and metabolomics data, we found that lactate, pyruvate, WASF3, RAB7B, and METTL7A might be used as novel biomarkers and potential therapeutic targets for BC. In addition, the disorders of the Krebs cycle pathway might affect the progression of BC.

Thyroid cancer is a common endocrine malignancy; however, surgery remains its primary treatment option. A novel targeted drug for the development and application of targeted therapy in thyroid cancer treatment remain underexplored. We obtained RNA sequence data of thyroid cancer from The Cancer Genome Atlas database and identified differentially expressed genes (DEGs). Then, we constructed co-expression network with DEGs and combined it with differentially methylation analysis to screen the key genes in thyroid cancer. PockDrug-Server, an online tool, was applied to predict the druggability of the key genes. Finally, we constructed protein-protein interaction (PPI) network to observe potential targeted drugs for thyroid cancer. We identified 3 genes correlated with altered DNA methylation level and oncogenesis of thyroid cancer. According to the druggable analysis and PPI network, we predicted TRAF2 and NCK-interacting protein kinase (TNIK) sever as the drug targeted for thyroid cancer. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis indicated that genes in protein-protein interaction network of TNIK enriched in mitogen-activated protein kinase signaling pathway. For drug repositioning, we identified a targeted drug of genes in PPI network. Our study provides a bioinformatics method for screening drug targets and provides a theoretical basis for thyroid cancer targeted therapy.

We investigated the association between single nucleotide polymorphisms (SNPs) in the fat mass and obesity associated (FTO) gene (rs9926289 A/G, rs79206939 A/G, rs9930506 A/G, rs8050136 A/C, and rs1588413 C/T) and polycystic ovary syndrome (PCOS), as well as outcomes of in vitro fertilization (IVF). A case-control study consisting of 147 PCOS patients and 120 healthy controls was conducted. FTO SNPs were genotyped by PCR to determine allelic frequencies, and IVF outcomes were analyzed. The results showed that FTO rs8050136 (p = .025) and rs1588413 (p = .042) were significantly associated with PCOS susceptibility, and women with risk alleles were often found to be obese (p

It is very important but difficult to identify which genes in gene expression data can contribute most to tumor subtype classification. An approach to select a small subset of genes for leukemia subtype classification from large scale gene expression profile is proposed in this paper. Having removed the noisy genes with little relevance to the classification task, the "sequential floating forward search" method was employed to generate candidate feature subsets consisting of informative genes, and then, a support vector machine was employed as a classifier to select the optimal feature subset with minimum classification errors. The results of our experiment showed that all the samples can be correctly classified without any error with only five genes.

Study on the relationship between Helicobacter pylori infection and proliferative kinetics of gastric mucosa