Research Progress of Gliomas in Machine Learning

In the field of gliomas research, the broad availability of genetic and image information originated by computer technologies and the booming of biomedical publications has led to the advent of the big-data era. Machine learning methods were applied as possible approaches to speed up the data mining processes. In this article, we reviewed the present situation and future orientations of machine learning application in gliomas within the context of workflows to integrate analysis for precision cancer care. Publicly available tools or algorithms for key machine learning technologies in the literature mining for glioma clinical research were reviewed and compared. Further, the existing solutions of machine learning methods and their limitations in glioma prediction and diagnostics, such as overfitting and class imbalanced, were critically analyzed.

ASCO 2021—an update on metastastic colorectal cancer

SummaryThe 2021 ASCO Annual Meeting provided updates on novel therapies in rare subgroups of metastatic colorectal cancer, such as immunotherapy in microsatellite instable colorectal cancer and antibody–drug conjugate therapy in HER2-positive disease. Furthermore, the concept of anti-EGFR rechallenge therapy has received additional momentum with data from the CHRONOS trial in regard to treating patients in later lines as well as how to integrate analysis of circulating tumor DNA in clinical decision-making.

Integrate analysis of the prognostic values of COL4As to human gastric cancer

Collagen type IV (Col IV) is the main constituent of the basement membrane. Under physiological conditions, Col IV plays an important role in maintaining epithelial integrity and stabilizing epithelial function of the gastric mucosa. Under pathological conditions, Col IV can be free from the basement membrane under the influence of tumor cells and play a pro-metastatic role. Although there is a increasing number of investigations on Col IV, no studies to date have directly uncovered the prognostic role and potential regulatory role of the six isoforms of Col IV in gastric cancer. In the present experiment, we aimed to analyze the role of COL4A family genes in gastric cancer. COL4A1/2/3/4 was significantly overexpressed, while COL4A5/6 was decreased in gastric cancer tissues according to TCGA data and our immunohistochemical staining results. And COL4A1 had a positive correlation with tumor stage, while COL4A5/6 had negative correlations with tumor stage. COL4A1/2/4/5/6 can be considered as a diagnostic indicator of gastric cancer. High levels of COL4A1/2/4/5/6 expression may be predictive of a poor prognosis of gastric cancer. The percentages of genetic alterations in COL4As for stomach cancer varied from 3 to 17% based on the TCGA data (COL4A1, 10%; COL4A2, 8%; COL4A3, 3%; COL4A4, 5%; COL4A5, 17%; COL4A6, 17%). Besides, COL4As may modulate tumor progression by participating in classical cancer pathways: COL4A1/2/3/4/5/6 can activate the EMT process; COL4A2/3/4/6 can inhibit apoptosis and cell cycle; COL4A3/4/5 can activate the PI3K/AKT signaling pathway. This study implied that COL4As have diagnostic and prognostic value for gastric cancer.

European Cohesion Policy performance and citizens’ awareness: A holistic System Dynamics framework

As Cohesion Policy constitutes the major funding scheme of the European Union, not only does literature explore if the policy’s performance is satisfactory but as well investigates the extent to which the policy is effectively communicated to citizens. To integrate analysis of implementation and communication, we develop a novel qualitative framework that elicits a holistic analysis of the causal mechanisms behind: (i) the distribution of the Cohesion Policy funds, their management at a local managing authority level and the related impact on projects’ quality, and (ii) the communication processes that underpin citizens’ awareness about the Union’s role in funded projects. The multilevel nature and the dynamic behaviour of the system, as well as its multiple feedback loops, render System Dynamics as the appropriate approach to model its complexity. The proposed framework aims at stimulating a focused discussion on Cohesion Policy through providing policy-making insights for designing efficient schemes to improve not only actual performance but, more importantly, perceived performances, as well as for supporting research in the field from a new organisational point of view.

Integrate analysis and identification for different expression genes in chondrogenesis

Background: The intricate mechanisms of articular chondrogenesis are largely unknown. Gradually, with the help of high-throughput platforms, microarrays have become an important and useful method to testify hub genes in desease. Today, advanced bioinformatic analysis of available microarray data can provide more reliable and accurate screening results by duplicating related data sets. Results: Microarray datasets GSE9451 and GSE104113 were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were performed, and function enrichment analyses were demonstrated. The protein-protein interaction network (PPI) was constructed and the module analysis was performed by using STRING and Cytoscape. Quantitative PCR was used to confirm the results of bioinformatics analysis. Conclusion: Compared to individual studies, this study can provide extra reliable and accurate screening results by duplicating relevant records. Additional molecular experiments are required to confirm the discovery of candidate genes identified by chondrogenesis. S100A4 is predicted to integrate with miR-325-3p to promote osteogenesis.

Mobile Worlds: Choice at the Intersection of Demographic and Environmental Change

Research on environmental change has often focused on changes in population as a significant driver of unsustainability and environmental degradation. Demographic pessimism and limited engagement with demographic realities underpin many arguments concerning limits to growth, environmental refugees, and environment-related conflicts. Re-engagement between demographic and environmental sciences has led to greater understanding of the interactions between the size, composition, and distribution of populations and exposure to environmental risks and contributions to environmental burdens. We review the results of this renewed and far more nuanced research frontier, focusing in particular on the way demographic trends affect exposure, sensitivity, and adaptation to environmental change. New research has explained how migration systems interact with environmental challenges in individual decisions and in globally aggregate flows. Here we integrate analysis on demographic and environmental risks that often share a root cause in limited social freedoms and opportunities. We argue for a capabilities approach to promoting sustainable solutions for a more mobile world.

RegTools: Integrated analysis of genomic and transcriptomic data for the discovery of splicing variants in cancer

Somatic mutations in non-coding regions and even in exons may have unidentified regulatory consequences which are often overlooked in analysis workflows. Here we present RegTools (www.regtools.org), a free, open-source software package designed to integrate analysis of somatic variants from genomic data with splice junctions from transcriptomic data to identify variants that may cause aberrant splicing. RegTools was applied to over 9,000 tumor samples with both tumor DNA and RNA sequence data. We discovered 235,778 events where a variant significantly increased the splicing of a particular junction, across 158,200 unique variants and 131,212 unique junctions. To characterize these somatic variants and their associated splice isoforms, we annotated them with the Variant Effect Predictor (VEP), SpliceAI, and Genotype-Tissue Expression (GTEx) junction counts and compared our results to other tools that integrate genomic and transcriptomic data. While certain events can be identified by the aforementioned tools, the unbiased nature of RegTools has allowed us to identify novel splice variants and previously unreported patterns of splicing disruption in known cancer drivers, such as TP53, CDKN2A, and B2M, as well as in genes not previously considered cancer-relevant, such as RNF145.