The timing and nature of the arrival and the subsequent expansion of modern humans into eastern Asia remains controversial. Using Y-chromosome biallelic markers, we investigated the ancient human-migration patterns in eastern Asia. Our data indicate that southern populations in eastern Asia are much more polymorphic than northern populations, which have only a subset of the southern haplotypes. This pattern indicates that the first settlement of modern humans in eastern Asia occurred in mainland Southeast Asia during the last Ice Age, coinciding with the absence of human fossils in eastern Asia, 50,000-100,000 years ago. After the initial peopling, a great northward migration extended into northern China and Siberia.

Although substantial progress has been made in cancer biology and treatment, clinical outcomes of bladder carcinoma (BC) patients are still not satisfactory. The tumor microenvironment (TME) is a potential target. Here, by single-cell RNA sequencing on 8 BC tumor samples and 3 para tumor samples, we identify 19 different cell types in the BC microenvironment, indicating high intra-tumoral heterogeneity. We find that tumor cells down regulated MHC-II molecules, suggesting that the downregulated immunogenicity of cancer cells may contribute to the formation of an immunosuppressive microenvironment. We also find that monocytes undergo M2 polarization in the tumor region and differentiate. Furthermore, the LAMP3 + DC subgroup may be able to recruit regulatory T cells, potentially taking part in the formation of an immunosuppressive TME. Through correlation analysis using public datasets containing over 3000 BC samples, we identify a role for inflammatory cancer-associated fibroblasts (iCAFs) in tumor progression, which is significantly related to poor prognosis. Additionally, we characterize a regulatory network depending on iCAFs. These results could help elucidate the protumor mechanisms of iCAFs. Our results provide deep insight into cancer immunology and provide an essential resource for drug discovery in the future.

Gene expression studies bridge the gap between DNA information and trait information by dissecting biochemical pathways into intermediate components between genotype and phenotype. These studies open new avenues for identifying complex disease genes and biomarkers for disease diagnosis and for assessing drug efficacy and toxicity. However, the majority of analytical methods applied to gene expression data are not efficient for biomarker identification and disease diagnosis. In this paper, we propose a general framework to incorporate feature (gene) selection into pattern recognition in the process to identify biomarkers. Using this framework, we develop three feature wrappers that search through the space of feature subsets using the classification error as measure of goodness for a particular feature subset being "wrapped around": linear discriminant analysis, logistic regression, and support vector machines. To effectively carry out this computationally intensive search process, we employ sequential forward search and sequential forward floating search algorithms. To evaluate the performance of feature selection for biomarker identification we have applied the proposed methods to three data sets. The preliminary results demonstrate that very high classification accuracy can be attained by identified composite classifiers with several biomarkers.

Objective-Proprotein convertase subtilisin/kexin type 9 (PCSK9) negatively regulates the low-density lipoprotein (LDL) receptor (LDLR) in hepatocytes and therefore plays an important role in controlling circulating levels of LDL-cholesterol. To date, the relationship between PCSK9 and metabolism of apolipoprotein B (apoB), the structural protein of LDL, has been controversial and remains to be clarified. Methods and Results-We assessed the impact of PCSK9 overexpression (≈400-fold above baseline) on apoB synthesis and secretion in 3 mouse models: wild-type C57BL/6 mice and LDLR-null mice (Ldlr −/− and Ldlr). Irrespective of LDLR expression, mice transduced with the PCSK9 gene invariably exhibited increased levels of plasma cholesterol, triacylglycerol, and apoB. Consistent with these findings, the levels of very-low-density lipoprotein and LDL were also increased whereas high-density lipoprotein levels were unchanged. Importantly, we demonstrated that endogenous PCSK9 interacted with apoB in hepatocytes. The PCSK9/apoB interaction resulted in increased production of apoB, possibly through the inhibition of intracellular apoB degradation via the autophagosome/lysosome pathway. Conclusion-We propose a new role for PCSK9 that involves shuttling between apoB and LDLR. The present study thus provides new insights into the action of PCSK9 in regulating apoB metabolism.