Background: The analysis of cancer diversity based on a logical framework of hallmarks has greatly improved our understanding of the occurrence, development and metastasis of various cancers. Methods: We designed Cancer Hallmark Genes (CHG) database which focuses on integrating hallmark genes in a systematic, standard way and annotates the potential roles of the hallmark genes in cancer processes. Following the conceptual criteria description of hallmark function the keywords for each hallmark were manually selected from the literature. Candidate hallmark genes collected were derived from 301 pathways of KEGG database by Lucene and manually corrected. Results: Based on the variation data, we finally identified the hallmark genes of various types of cancer and constructed CHG. And we also analyzed the relationships among hallmarks and potential characteristics and relationships of hallmark genes based on the topological structures of their networks. We manually confirm the hallmark gene identified by CHG based on literature and database. We also predicted the prognosis of breast cancer, glioblastoma multiforme and kidney papillary cell carcinoma patients based on CHG data. Conclusions: In summary, CHG, which was constructed based on a hallmark feature set, provides a new perspective for analyzing the diversity and development of cancers.

Background: Angiogenesis is critical for breast cancer progression. Within tumors, non-neoplastic cells assist tumor growth by producing growth factors and pro-angiogenic cytokines. Studies have demonstrated that tumor-associated macrophages (TAMs) are recruited to tumors before malignant conversion and are essential for promoting angiogenesis. We sought to study the role that macrophage phenotype—classically activated (M1) and alternatively activated (M2)—plays in the subsequent activation of the angiogenic pathway, with the goal of understanding the mechanisms underlying angiogenesis in the different molecular subtypes of breast cancer.Methods: 128 matching breast tumors, DCIS and normal tissues were obtained from the University of Chicago Breast Cancer SPORE tissue bank under IRB approved protocols. Tissue microarrays were constructed and molecular subtype was assigned based on immunohistochemical (IHC) staining into the following groups: luminal A (ER+, PR+, HER2-), luminal B (ER+, HER2+ or ER+, PR-), HER2-like (ER-, HER2+) and basal-like (ER-, HER2-, EGFR+ and/or CK5/6+). Macrophage phenotype was determined using double staining with CD68/CD163 (M2) and CD68/CD80 (M1). Microvessel density (MVD) was measured by IHC staining using anti-CD34. Staining quantification was performed independently by two pathologists. To control for intra-individual correlation, linear mixed-effects models were used to compare differences in % of M1 and M2 with disease progression. To evaluate the association of MVD with % of M1 and M2, bivariate plots were generated and Pearson's correlation coefficients were calculated. Spearman's correlation coefficients were used for the correlation between macrophage phenotype and tumor stage and grade. The Kaplan-Meier method was used to calculate overall survival.Results: Of the tumors studied, 88% were stage I-II. 17% were grade 1, 39% grade 2 and 44% grade 3. 70 were luminal A, 36 basal-like, 9 HER2-like and 6 luminal B. The ratio of M2:M1 increased with disease progression from normal breast to DCIS to invasive cancer (p<0.001). Increased M2% was associated with high tumor grade, increased MVD and decreased overall survival (all p<0.001). M1% was associated with low tumor grade (<0.001), but was not significantly associated with MVD or overall survival. Both the HER2-like and basal-like subtypes have significantly higher % M2 as compared to the luminal A subtype (p<0.001).Discussion: There are several studies which suggest that activated TAMs are responsible for the secretion of pro-angiogenic cytokines which stimulate neovascularization. To our knowledge, this is the first study that has correlated macrophage phenotype to breast molecular subtype and MVD in human breast tumors. Our findings suggest that the M2 macrophage phenotype is associated with aggressive histopathologic features and poor clinical outcome. Inhibiting the M2 macrophage may prevent the release of pro-angiogenic factors, and might be an effective approach at preventing neovascularization and improving patient outcomes. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 107.

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Idiosyncratic adverse drug reactions are drug reactions that occur rarely and unpredictably among the population. These reactions often occur after a drug is marketed, which means that they are strongly related to the genotype of the population. The prediction of such adverse reactions is a major challenge because of the lack of appropriate test models during the drug development process. In this study, we chose withdrawn drugs because the reasons why they were withdrawn and from which countries or regions is easily obtained. We selected Dilevalol and its chiral drug (Labetalol) as the investigatory drugs, as they have been withdrawn from a European market (Britain) because of serious hepatotoxicity. First, we searched for and obtained the Dilevalol-induced- liver-injury related protein, multidrug resistance protein 1 (MDR1), from the Comparative Toxicogenomics Database (CTD). Then, we searched and extracted 477 non-synonymous single nucleotide polymorphisms (nsSNP) on MDR1 in the dbSNP database. Second, we used the VarMod tool to predict the functional changes of MDR1 induced by these nsSNPs, from which we extracted the nsSNPs that significantly change the functions of this protein. Third, we built the three-dimensional structures of those variant proteins and used AutoDock to perform a docking study, choosing the best model to determine the sites of nsSNPs. Finally, we used the data from the 1000 Genomes Project to verify the dominant population distribution of the risk SNP. We applied the same strategy to the post-marketing drug-induced liver injury drugs to further test the feasibility of our method.

Background: Long non-coding RNAs (lncRNAs) play an important role in the immune regulation of gastric cancer (GC). However, the clinical application value of immune-related lncRNAs has not been fully developed. It is of great significance to overcome the challenges of prognostic prediction and classification of gastric cancer patients based on the current study.Methods: In this study, the R package ImmLnc was used to obtain immune-related lncRNAs of The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) project, and univariate Cox regression analysis was performed to find prognostic immune-related lncRNAs. A total of 117 combinations based on 10 algorithms were integrated to determine the immune-related lncRNA prognostic model (ILPM). According to the ILPM, the least absolute shrinkage and selection operator (LASSO) regression was employed to find the major lncRNAs and develop the risk model. ssGSEA, CIBERSORT algorithm, the R package maftools, pRRophetic, and clusterProfiler were employed for measuring the proportion of immune cells among risk groups, genomic mutation difference, drug sensitivity analysis, and pathway enrichment score.Results: A total of 321 immune-related lncRNAs were found, and there were 26 prognostic immune-related lncRNAs. According to the ILPM, 18 of 26 lncRNAs were selected and the risk score (RS) developed by the 18-lncRNA signature had good strength in the TCGA training set and Gene Expression Omnibus (GEO) validation datasets. Patients were divided into high- and low-risk groups according to the median RS, and the low-risk group had a better prognosis, tumor immune microenvironment, and tumor signature enrichment score and a higher metabolism, frequency of genomic mutations, proportion of immune cell infiltration, and antitumor drug resistance. Furthermore, 86 differentially expressed genes (DEGs) between high- and low-risk groups were mainly enriched in immune-related pathways.Conclusion: The ILPM developed based on 26 prognostic immune-related lncRNAs can help in predicting the prognosis of patients suffering from gastric cancer. Precision medicine can be effectively carried out by dividing patients into high- and low-risk groups according to the RS.