The "guardian of the genome," TP53, is one of the most frequently mutated genes of all cancers. Despite the important biological roles of TP53, the clinical relevance of TP53 mutations, in gastric cancer (GC), remains largely unknown. Here, we systematically assessed clinical relevance, in terms of TP53 mutation positions, finding substantial variability. Thus, we hypothesized that the position of the TP53 mutation might affect clinical outcomes in GC. We systematically inspected missense mutations in TP53, from a TCGA (The Cancer Genome Atlas) GC dataset in UCSC Xena repository. Specifically, we examined five aspects of each mutational position: (1) the whole gene body; (2) known hot-spots; (3) the DNA-binding domain; (4) the secondary structure of the domain; and (5) individual mutation positions. We then analyzed the clinical outcomes for each aspect. These results showed that, in terms of secondary structure, patients with mutations in turn regions showed poor prognosis, compared to those with mutations in beta strand regions (log rank ${\text{p}}= {{0.043}}$p=0.043). Also, in terms of individual mutation positions, patients having mutations at R248 showed poorer survival than other patients having mutations at different TP53 positions (log rank ${\text{p}}= {{0.035}}$p=0.035).

MOTIVATION: Recent evidence shows significant involvement of microRNAs (miRNAs) in the initiation and progression of numerous cancers; however, the role of these in tumor drug resistance remains unknown. RESULTS: By comparing global miRNA and mRNA expression patterns, we examined the role of miRNAs in resistance to the 'pure antiestrogen' fulvestrant, using fulvestrant-resistant MCF7-FR cells and their drug-sensitive parental estrogen receptor (ER)-positive MCF7 cells. We identified 14 miRNAs downregulated in MCF7-FR cells and then used both TargetScan and PITA to predict potential target genes. We found a negative correlation between expression of these miRNAs and their predicted target mRNA transcripts. In genes regulated by multiple miRNAs or having multiple miRNA-targeting sites, an even stronger negative correlation was found. Pathway analyses predicted these miRNAs to regulate specific cancer-associated signal cascades. These results suggest a significant role for miRNA-regulated gene expression in the onset of breast cancer antiestrogen resistance, and an improved understanding of this phenomenon could lead to better therapies for this often fatal condition.

MOTIVATION: In the human genome, 'CpG islands', CG-rich regions located in or near gene promoters, are normally unmethylated. However, in cancer cells, CpG islands frequently gain methylation, resulting in silencing of growth-limiting tumor suppressor genes. To our knowledge, the potential relationship between CpG island hypermethylation, transcription factor (TF) binding in local promoter regions and transcriptional control has not been previously explored in a genome-wide context. RESULTS: In this study, we utilized bioinformatics tools and TF binding site(TFBs) databases to globally analyze sequences methylated in a laboratory model for the development of drug-resistant cancer. Our results demonstrated that four TFBS were enriched in hypermethylated sequences. More interestingly, overrepresentation of these TFBS was observed in hyper-/hypo-methylated sequences where signi.cant changes in methylation levels were observed in drug-resistant cancer cells. In summary, we believe that these.ndings offer a means to further explore the relationship between DNA methylation and gene expression in drug resistance and tumorigenesis.

MOTIVATION: To detect and select patterns of transcription factor binding sites (TFBSs) which distinguish genes directly regulated by estrogen receptor-alpha (ERalpha), we developed an innovative mixture model-based discriminate analysis for identifying ordered TFBS pairs. RESULTS: Biologically, our proposed new algorithm clearly suggests that TFBSs are not randomly distributed within ERalpha target promoters (P-value < 0.001). The up-regulated targets significantly (P-value < 0.01) possess TFBS pairs, (DBP, MYC), (DBP, MYC/MAX heterodimer), (DBP, USF2) and (DBP, MYOGENIN); and down-regulated ERalpha target genes significantly (P-value < 0.01) possess TFBS pairs, such as (DBP, c-ETS1-68), (DBP, USF2) and (DBP, MYOGENIN). Statistically, our proposed mixture model-based discriminate analysis can simultaneously perform TFBS pattern recognition, TFBS pattern selection, and target class prediction; such integrative power cannot be achieved by current methods. AVAILABILITY: The software is available on request from the authors. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.