Identification of cell surface markers for acute myeloid leukemia prognosis based on multi-model analysis.

Given the extremely high inter-patient heterogeneity among acute myeloid leukemia (AML), identifying biomarkers for prognostic assessment and therapeutic guidance is crucial. Cell surface markers (CSMs) have been shown to play an important role in AML leukemogenesis and progression. In this study, we evaluate the prognostic potential of all human CSMs in AML patients based on differential gene expression analysis and univariate Cox regression analysis. Utilizing multi-model analysis, including Adaptive LASSO regression, LASSO regression, and Elastic Net, we construct a 9-CSMs prognostic model for risk stratification of AML patients. The predictive value of the 9-CSMs risk score is further confirmed in three independent datasets. Multivariate Cox regression analysis shows that the risk score is an independent prognostic factor for AML patients. AML patients with high 9-CSMs risk scores have shorter overall and event-free survival time than those with lower scores. Notably, our single-cell RNA-seq analysis indicates that patients with high 9-CSMs risk scores exhibit chemotherapy resistance. Further, PI3K inhibitors are identified as potential treatments for these high-risk patients. In conclusion, we construct a 9-CSMs prognostic model which is an independent prognostic factor for the survival of AML patients and has the potential to guide drug therapy.

Natural Coevolution of Tumor and Immunoenvironment in Glioblastoma.

Isocitrate dehydrogenase (IDH) wild-type glioblastoma (GBM) has a dismal prognosis. A better understanding of tumor evolution holds the key to developing more effective treatment. Here we study GBM's natural evolutionary trajectory by using rare multifocal samples. We sequenced 61,062 single cells from eight multifocal IDH wild-type primary GBMs and defined a natural evolution signature (NES) of the tumor. We show that the NES significantly associates with the activation of transcription factors that regulate brain development, including MYBL2 and FOSL2. Hypoxia is involved in inducing NES transition potentially via activation of the HIF1A-FOSL2 axis. High-NES tumor cells could recruit and polarize bone marrow-derived macrophages through activation of the FOSL2-ANXA1-FPR1/3 axis. These polarized macrophages can efficiently suppress T-cell activity and accelerate NES transition in tumor cells. Moreover, the polarized macrophages could upregulate CCL2 to induce tumor cell migration. SIGNIFICANCE: GBM progression could be induced by hypoxia via the HIF1A-FOSL2 axis. Tumor-derived ANXA1 is associated with recruitment and polarization of bone marrow-derived macrophages to suppress the immunoenvironment. The polarized macrophages promote tumor cell NES transition and migration. This article is highlighted in the In This Issue feature, p. 2711.

An Integrating Approach for Genome-Wide Screening of MicroRNA Polymorphisms Mediated Drug Response Alterations.

MicroRNAs (miRNAs) are a class of evolutionarily conserved small noncoding RNAs, ~22 nt in length, and found in diverse organisms and play important roles in the regulation of mRNA translation and degradation. It was shown that miRNAs were involved in many key biological processes through regulating the expression of targets. Genetic polymorphisms in miRNA target sites may alter miRNA regulation and therefore result in the alterations of the drug targets. Recent studies have demonstrated that SNPs in miRNA target sites can affect drug efficiency. However, there are still a large number of specific genetic variants related to drug efficiency that are yet to be discovered. We integrated large scale of genetic variations, drug targets, gene interaction networks, biological pathways, and seeds region of miRNA to identify miRNA polymorphisms affecting drug response. In addition, harnessing the abundant high quality biological network/pathways, we evaluated the cascade distribution of tarSNP impacts. We showed that the predictions can uncover most of the known experimentally supported cases as well as provide informative candidates complementary to existing methods/tools. Although there are several existing databases predicting the gain or loss of targeting function of miRNA mediated by SNPs, such as PolymiRTS, miRNASNP, MicroSNiPer, and MirSNP, none of them evaluated the influences of tarSNPs on drug response alterations. We developed a user-friendly online database of this approach named Mir2Drug.

Integrated analysis identifies interaction patterns between small molecules and pathways.

Previous studies have indicated that the downstream proteins in a key pathway can be potential drug targets and that the pathway can play an important role in the action of drugs. So pathways could be considered as targets of small molecules. A link map between small molecules and pathways was constructed using gene expression profile, pathways, and gene expression of cancer cell line intervened by small molecules and then we analysed the topological characteristics of the link map. Three link patterns were identified based on different drug discovery implications for breast, liver, and lung cancer. Furthermore, molecules that significantly targeted the same pathways tended to treat the same diseases. These results can provide a valuable reference for identifying drug candidates and targets in molecularly targeted therapy.

Prioritizing cancer therapeutic small molecules by integrating multiple OMICS datasets.

Drug design is crucial for the effective discovery of anti-cancer drugs. The success or failure of drug design often depends on the leading compounds screened in pre-clinical studies. Many efforts, such as in vivo animal experiments and in vitro drug screening, have improved this process, but these methods are usually expensive and laborious. In the post-genomics era, it is possible to seek leading compounds for large-scale candidate small-molecule screening with multiple OMICS datasets. In the present study, we developed a computational method of prioritizing small molecules as leading compounds by integrating transcriptomics and toxicogenomics data. This method provides priority lists for the selection of leading compounds, thereby reducing the time required for drug design. We found 11 known therapeutic small molecules for breast cancer in the top 100 candidates in our list, 2 of which were in the top 10. Furthermore, another 3 of the top 10 small molecules were recorded as closely related to cancer treatment in the DrugBank database. A comparison of the results of our approach with permutation tests and shared gene methods demonstrated that our OMICS data-based method is quite competitive. In addition, we applied our method to a prostate cancer dataset. The results of this analysis indicated that our method surpasses both the shared gene method and random selection. These analyses suggest that our method may be a valuable tool for directing experimental studies in cancer drug design, and we believe this time- and cost-effective computational strategy will be helpful in future studies in cancer therapy.

A novel method to quantify gene set functional association based on gene ontology.

Numerous gene sets have been used as molecular signatures for exploring the genetic basis of complex disorders. These gene sets are distinct but related to each other in many cases; therefore, efforts have been made to compare gene sets for studies such as those evaluating the reproducibility of different experiments. Comparison in terms of biological function has been demonstrated to be helpful to biologists. We improved the measurement of semantic similarity to quantify the functional association between gene sets in the context of gene ontology and developed a web toolkit named Gene Set Functional Similarity (GSFS; http://bioinfo.hrbmu.edu.cn/GSFS). Validation based on protein complexes for which the functional associations are known demonstrated that the GSFS scores tend to be correlated with sequence similarity scores and that complexes with high GSFS scores tend to be involved in the same functional catalogue. Compared with the pairwise method and the annotation method, the GSFS shows better discrimination and more accurately reflects the known functional catalogues shared between complexes. Case studies comparing differentially expressed genes of prostate tumour samples from different microarray platforms and identifying coronary heart disease susceptibility pathways revealed that the method could contribute to future studies exploring the molecular basis of complex disorders.

Prioritizing human cancer microRNAs based on genes' functional consistency between microRNA and cancer.

The identification of human cancer-related microRNAs (miRNAs) is important for cancer biology research. Although several identification methods have achieved remarkable success, they have overlooked the functional information associated with miRNAs. We present a computational framework that can be used to prioritize human cancer miRNAs by measuring the association between cancer and miRNAs based on the functional consistency score (FCS) of the miRNA target genes and the cancer-related genes. This approach proved successful in identifying the validated cancer miRNAs for 11 common human cancers with area under ROC curve (AUC) ranging from 71.15% to 96.36%. The FCS method had a significant advantage over miRNA differential expression analysis when identifying cancer-related miRNAs with a fine regulatory mechanism, such as miR-27a in colorectal cancer. Furthermore, a case study examining thyroid cancer showed that the FCS method can uncover novel cancer-related miRNAs such as miR-27a/b, which were showed significantly upregulated in thyroid cancer samples by qRT-PCR analysis. Our method can be used on a web-based server, CMP (cancer miRNA prioritization) and is freely accessible at http://bioinfo.hrbmu.edu.cn/CMP. This time- and cost-effective computational framework can be a valuable complement to experimental studies and can assist with future studies of miRNA involvement in the pathogenesis of cancers.

A novel network-based method for measuring the functional relationship between gene sets.

MOTIVATION: In the functional genomic era, a large number of gene sets have been identified via high-throughput genomic and proteomic technologies. These gene sets of interest are often related to the same or similar disorders or phenotypes, and are commonly presented as differentially expressed gene lists, co-expressed gene modules, protein complexes or signaling pathways. However, biologists are still faced by the challenge of comparing gene sets and interpreting the functional relationships between gene sets into an understanding of the underlying biological mechanisms. RESULTS: We introduce a novel network-based method, designated corrected cumulative rank score (CCRS), which analyzes the functional communication and physical interaction between genes, and presents an easy-to-use web-based toolkit called GsNetCom to quantify the functional relationship between two gene sets. To evaluate the performance of our method in assessing the functional similarity between two gene sets, we analyzed the functional coherence of complexes in functional catalog and identified protein complexes in the same functional catalog. The results suggested that CCRS can offer a significant advance in addressing the functional relationship between different gene sets compared with several other available tools or algorithms with similar functionality. We also conducted the case study based on our method, and succeeded in prioritizing candidate leukemia-associated protein complexes and expanding the prioritization and analysis of cancer-related complexes to other cancer types. In addition, GsNetCom provides a new insight into the communication between gene modules, such as exploring gene sets from the perspective of well-annotated protein complexes. AVAILABILITY AND IMPLEMENTATION: GsNetCom is a freely available web accessible toolkit at http://bioinfo.hrbmu.edu.cn/GsNetCom.

Functional homogeneity in microRNA target heterogeneity--a new sight into human microRNomics.

MicroRNomics is a novel genomics that studies the identification, targets, biological functions, etc., of microRNAs (miRNAs) on a genomic scale. Computational target prediction algorithms are important applications in microRNomics. However, the overlaps between target sets predicted by different algorithms for one miRNA are often small. Our work is initiated to find the reasons causing "heterogeneity" and investigate whether the heterogeneous targets are homogeneous on functional levels by integrating similarity metrics. The results suggest that most human miRNAs own heterogeneous targets. The dissimilarity of thermodynamic characteristics and the different treatment of 3'-compensatory sites adopted by algorithms are the main reasons for target "heterogeneity." Meanwhile, we find most miRNA heterogeneous targets are functional homogeneity because of the common principles such as sites conservation and G:U wobble pairs in different algorithms. Our findings reveal the "functional homogeneity in miRNA target heterogeneity." The conclusions provide a perspective of microRNomics on functional levels, which introduce a new sight into human miRNA targets.

Systematic analysis of human microRNA divergence based on evolutionary emergence.

MicroRNAs (miRNAs) play important roles in post-transcriptional gene expression control. To gain new insight into human miRNAs, we performed comprehensive sequence-based homology search for known human miRNAs to study the evolutionary distribution of human miRNAs. Furthermore, we carried out a series of studies to compare various features for different lineage-specific human miRNAs. Our results showed that major expansions of human miRNA genes coincide with the advent of vertebrates, mammals and primates. Further system-level analysis revealed significant differences in human miRNAs that arose from different evolutionary time points for a number of characteristics, implicating genetic and functional diversification for different human miRNAs during evolution. Our finds provide more useful knowledge for further exploring origins and evolution of human miRNA genes.

Single-nucleotide polymorphism-gene intermixed networking reveals co-linkers connected to multiple gene expression phenotypes.

Gene expression profiles and single-nucleotide polymorphism (SNP) profiles are modern data for genetic analysis. It is possible to use the two types of information to analyze the relationships among genes by some genetical genomics approaches. In this study, gene expression profiles were used as expression traits. And relationships among the genes, which were co-linked to a common SNP(s), were identified by integrating the two types of information. Further research on the co-expressions among the co-linked genes was carried out after the gene-SNP relationships were established using the Haseman-Elston sib-pair regression. The results showed that the co-expressions among the co-linked genes were significantly higher if the number of connections between the genes and a SNP(s) was more than six. Then, the genes were interconnected via one or more SNP co-linkers to construct a gene-SNP intermixed network. The genes sharing more SNPs tended to have a stronger correlation. Finally, a gene-gene network was constructed with their intensities of relationships (the number of SNP co-linkers shared) as the weights for the edges.