Demographics will reverse three multi-decade global trends

Between the 1980s and the 2000s, the largest ever positive labour supply shock occurred, resulting from demographic trends and from the inclusion of China and eastern Europe into the World Trade Organization. This led to a shift in manufacturing to Asia, especially China; a stagnation in real wages; a collapse in the power of private sector trade unions; increasing inequality within countries, but less inequality between countries; deflationary pressures; and falling interest rates. This shock is now reversing. As the world ages, real interest rates will rise, inflation and wage growth will pick up and inequality will fall. What is the biggest challenge to our thesis? The hardest prior trend to reverse will be that of low interest rates, which have resulted in a huge and persistent debt overhang, apart from some deleveraging in advanced economy banks. Future problems may now intensify as the demographic structure worsens, growth slows, and there is little stomach for major inflation. Are we in a trap where the debt overhang enforces continuing low interest rates, and those low interest rates encourage yet more debt finance? There is no silver bullet, but we recommend policy measures to switch from debt to equity finance

Transcriptional Analysis of Aggressiveness and Heterogeneity across Grades of Astrocytomas

<div><p>Astrocytoma is the most common glioma, accounting for half of all primary brain and spinal cord tumors. Late detection and the aggressive nature of high-grade astrocytomas contribute to high mortality rates. Though many studies identify candidate biomarkers using high-throughput transcriptomic profiling to stratify grades and subtypes, few have resulted in clinically actionable results. This shortcoming can be attributed, in part, to pronounced lab effects that reduce signature robustness and varied individual gene expression among patients with the same tumor. We addressed these issues by uniformly preprocessing publicly available transcriptomic data, comprising 306 tumor samples from three astrocytoma grades (Grade 2, 3, and 4) and 30 non-tumor samples (normal brain as control tissues). Utilizing Differential Rank Conservation (DIRAC), a network-based classification approach, we examined the global and individual patterns of network regulation across tumor grades. Additionally, we applied gene-based approaches to identify genes whose expression changed consistently with increasing tumor grade and evaluated their robustness across multiple studies using statistical sampling. Applying DIRAC, we observed a global trend of greater network dysregulation with increasing tumor aggressiveness. Individual networks displaying greater differences in regulation between adjacent grades play well-known roles in calcium/PKC, EGF, and transcription signaling. Interestingly, many of the 90 individual genes found to monotonically increase or decrease with astrocytoma grade are implicated in cancer-affected processes such as calcium signaling, mitochondrial metabolism, and apoptosis. The fact that specific genes monotonically increase or decrease with increasing astrocytoma grade may reflect shared oncogenic mechanisms among phenotypically similar tumors. This work presents statistically significant results that enable better characterization of different human astrocytoma grades and hopefully can contribute towards improvements in diagnosis and therapy choices. Our results also identify a number of testable hypotheses relating to astrocytoma etiology that may prove helpful in developing much-needed biomarkers for earlier disease detection.</p></div

Genes showing consistent differential expression with progression.

<p>Colors on the heatmap represent relative expression values of genes in different phenotypes. The vertical axis lists the differentially expressed genes and the horizontal axis lists the phenotypes. All expression values are normalized as the percentage of maximum expression value for the gene across all phenotypes. For the up-regulated genes in panel <b>A)</b> the maximum expression is either sGBM or pGBM, so we see all genes have brightest color in these two phenotypes; similarly the down-regulated genes in panel <b>B)</b> decrease their expression systematically from normal to GBM, and the intensity level also increases with grade.</p

Overview of approach.

<p><b>A)</b> We minimized experimental variation due to lab effects by performing uniform pre-processing. <b>B)</b> Genes that either monotonically increased or decreased in parallel with increasing astrocytoma grade were identified. <b>C)</b> Molecular signatures that can accurately distinguish between different grades were established using Differential Rank Conservation (DIRAC). We also examined broad patterns of network regulation across all astrocytoma grades.</p

Classification accuracy with BioCarta database networks.

<p>This heatmap displays leave-one-out cross-validation accuracies of DIRAC-based classifications on each phenotype vs. all other phenotypes. DIRAC could distinguish more distant grades like normal vs. GBMs; it is especially hard to separate G3 or pGBM from sGBM.</p

Functional categories among monotonically changing genes.

<p>Functional categories among monotonically changing genes.</p

Network-level expression heterogeneity across tumor grades.

<p><b>A)</b> Global trend of network regulation level decreases with increeasing grade. The vertical axis represents examined networks, while the horizontal axis represents five phenotypes. Colors represent rank conservation indices for each network. Light colors indicate high consistency of network ranking in a phenotype and the dark colors indicate large heterogeneity of networks. Networks in sGBM and pGBM tumors become much more heterogeneous compared to the normal cases. <b>B)</b> One-way ANOVA comparing the mean rank conservation values of different phenotypes. <b>C)</b> A list of most deregulated networks between adjacent tumor grades and their major biological functions. The “>” and “<” indicate the magnitude of network regulation. For instance, AKAP13 has a larger rank conservation index in normal samples and thus is more regulated in normal compared to G2 patients.</p

Top networks selected by DIRAC to classify tumor grades vs. normal brains (<i>P-value</i> <0.0001).

<p>Top 10 networks for G2 vs. N (top left), G3 vs. N (top right), sGBM vs. N (bottom left), and pGBM vs. N (bottom right).</p

Summary of microarray expression datasets included in the study.

a<p>Includes one normal fetal brain RNA, one normal cerebellum RNA, and two normal tissues surgically removed tissue adjacent to resected tumor tissue and RNA extracted.</p>b<p>Brain samples of epilepsy patients.</p>c<p>Pooled normal brain tissue.</p

Additional file 4: of A novel miR-375-HOXB3-CDCA3/DNMT3B regulatory circuitry contributes to leukemogenesis in acute myeloid leukemia

Table S3. Multivariate analyses of overall survival (OS) and disease-free survival (DFS) of AML. (DOCX 19 kb