scholarly journals Recognizing Pattern and Rule of Mutation Signatures Corresponding to Cancer Types

2021 ◽  
Vol 9 ◽  
Author(s):  
Lei Chen ◽  
Xianchao Zhou ◽  
Tao Zeng ◽  
Xiaoyong Pan ◽  
Yu-Hang Zhang ◽  
...  
Keyword(s):  
Feature Selection ◽  
Recent Literature ◽  
Genetic Mutations ◽  
Computational Investigation ◽  
Incremental Feature Selection ◽  
Cancer Mutation ◽  
Feature List ◽  
Different Types ◽  
Cancer Types ◽  
Monte Carlo Feature Selection

Cancer has been generally defined as a cluster of systematic malignant pathogenesis involving abnormal cell growth. Genetic mutations derived from environmental factors and inherited genetics trigger the initiation and progression of cancers. Although several well-known factors affect cancer, mutation features and rules that affect cancers are relatively unknown due to limited related studies. In this study, a computational investigation on mutation profiles of cancer samples in 27 types was given. These profiles were first analyzed by the Monte Carlo Feature Selection (MCFS) method. A feature list was thus obtained. Then, the incremental feature selection (IFS) method adopted such list to extract essential mutation features related to 27 cancer types, find out 207 mutation rules and construct efficient classifiers. The top 37 mutation features corresponding to different cancer types were discussed. All the qualitatively analyzed gene mutation features contribute to the distinction of different types of cancers, and most of such mutation rules are supported by recent literature. Therefore, our computational investigation could identify potential biomarkers and prediction rules for cancers in the mutation signature level.

BioDog, biomarker detection for improving identification power of breast cancer histologic grade in methylomics

Epigenomics ◽  
2019 ◽  
Vol 11 (15) ◽  
pp. 1717-1732 ◽  
Author(s):  
Yexian Zhang ◽  
Chaorong Chen ◽  
Meiyu Duan ◽  
Shuai Liu ◽  
Lan Huang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Prediction Accuracy ◽  
Somatic Mutations ◽  
Bias Reduction ◽  
Histologic Grade ◽  
Biomarker Detection ◽  
Reduction Strategy ◽  
Incremental Feature Selection ◽  
Cancer Types

Aim: Breast cancer histologic grade (HG) is a well-established prognostic factor. This study aimed to select methylomic biomarkers to predict breast cancer HGs. Materials & methods: The proposed algorithm BioDog firstly used correlation bias reduction strategy to eliminate redundant features. Then incremental feature selection was applied to find the features with a high HG prediction accuracy. The sequential backward feature elimination strategy was employed to further refine the biomarkers. A comparison with existing algorithms were conducted. The HG-specific somatic mutations were investigated. Results & conclusions: BioDog achieved accuracy 0.9973 using 92 methylomic biomarkers for predicting breast cancer HGs. Many of these biomarkers were within the genes and lncRNAs associated with the HG development in breast cancer or other cancer types.

scholarly journals Alternative Polyadenylation Modification Patterns Reveal Essential Posttranscription Regulatory Mechanisms of Tumorigenesis in Multiple Tumor Types

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Min Li ◽  
XiaoYong Pan ◽  
Tao Zeng ◽  
Yu-Hang Zhang ◽  
Kaiyan Feng ◽  
...  
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Alternative Polyadenylation ◽  
Machine Learning Algorithms ◽  
Regulatory Mechanisms ◽  
Support Vector ◽  
Transcriptional Level ◽  
Cancer Data ◽  
Feature List ◽  
Cancer Types

Among various risk factors for the initiation and progression of cancer, alternative polyadenylation (APA) is a remarkable endogenous contributor that directly triggers the malignant phenotype of cancer cells. APA affects biological processes at a transcriptional level in various ways. As such, APA can be involved in tumorigenesis through gene expression, protein subcellular localization, or transcription splicing pattern. The APA sites and status of different cancer types may have diverse modification patterns and regulatory mechanisms on transcripts. Potential APA sites were screened by applying several machine learning algorithms on a TCGA-APA dataset. First, a powerful feature selection method, minimum redundancy maximum relevancy, was applied on the dataset, resulting in a feature list. Then, the feature list was fed into the incremental feature selection, which incorporated the support vector machine as the classification algorithm, to extract key APA features and build a classifier. The classifier can classify cancer patients into cancer types with perfect performance. The key APA-modified genes had a potential prognosis ability because of their significant power in the survival analysis of TCGA pan-cancer data.

scholarly journals Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms

2019 ◽  
Vol 20 (9) ◽  
pp. 2185 ◽  
Author(s):  
Xiaoyong Pan ◽  
Lei Chen ◽  
Kai-Yan Feng ◽  
Xiao-Hua Hu ◽  
Yu-Hang Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Expression Pattern ◽  
Learning Algorithms ◽  
Expression Patterns ◽  
Feature Selection Method ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Feature List ◽  
Cancer Types

Small nucleolar RNAs (snoRNAs) are a new type of functional small RNAs involved in the chemical modifications of rRNAs, tRNAs, and small nuclear RNAs. It is reported that they play important roles in tumorigenesis via various regulatory modes. snoRNAs can both participate in the regulation of methylation and pseudouridylation and regulate the expression pattern of their host genes. This research investigated the expression pattern of snoRNAs in eight major cancer types in TCGA via several machine learning algorithms. The expression levels of snoRNAs were first analyzed by a powerful feature selection method, Monte Carlo feature selection (MCFS). A feature list and some informative features were accessed. Then, the incremental feature selection (IFS) was applied to the feature list to extract optimal features/snoRNAs, which can make the support vector machine (SVM) yield best performance. The discriminative snoRNAs included HBII-52-14, HBII-336, SNORD123, HBII-85-29, HBII-420, U3, HBI-43, SNORD116, SNORA73B, SCARNA4, HBII-85-20, etc., on which the SVM can provide a Matthew’s correlation coefficient (MCC) of 0.881 for predicting these eight cancer types. On the other hand, the informative features were fed into the Johnson reducer and repeated incremental pruning to produce error reduction (RIPPER) algorithms to generate classification rules, which can clearly show different snoRNAs expression patterns in different cancer types. The analysis results indicated that extracted discriminative snoRNAs can be important for identifying cancer samples in different types and the expression pattern of snoRNAs in different cancer types can be partly uncovered by quantitative recognition rules.

scholarly journals Dual Roles of the Activin Signaling Pathway in Pancreatic Cancer

Biomedicines ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 821
Author(s):  
Wanglong Qiu ◽  
Chia-Yu Kuo ◽  
Yu Tian ◽  
Gloria H. Su
Keyword(s):  
Pancreatic Cancer ◽  
Signaling Pathway ◽  
Critical Role ◽  
Genetic Mutations ◽  
Cancer Genetic ◽  
Dual Roles ◽  
Activin Signaling ◽  
Physiological Processes ◽  
Cancer Types ◽  
Related Proteins

Activin, a member of the TGF-β superfamily, is involved in many physiological processes, such as embryonic development and follicle development, as well as in multiple human diseases including cancer. Genetic mutations in the activin signaling pathway have been reported in many cancer types, indicating that activin signaling plays a critical role in tumorigenesis. Recent evidence reveals that activin signaling may function as a tumor-suppressor in tumor initiation, and a promoter in the later progression and metastasis of tumors. This article reviews many aspects of activin, including the signaling cascade of activin, activin-related proteins, and its role in tumorigenesis, particularly in pancreatic cancer development. The mechanisms regulating its dual roles in tumorigenesis remain to be elucidated. Further understanding of the activin signaling pathway may identify potential therapeutic targets for human cancers and other diseases.

scholarly journals Lights and Shadows in Immuno-Oncology Drug Development

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 691
Author(s):  
Milana Bergamino Sirvén ◽  
Sonia Pernas ◽  
Maggie C. U. Cheang
Keyword(s):  
Drug Development ◽  
Checkpoint Inhibitors ◽  
Late Phase ◽  
New Drugs ◽  
Successful Outcome ◽  
Different Types ◽  
Cancer Types ◽  
Clinical Trial Designs ◽  
Oncology Drug Development ◽  
Critical Aspects

The rapidly evolving landscape of immuno-oncology (IO) is redefining the treatment of a number of cancer types. IO treatments are becoming increasingly complex, with different types of drugs emerging beyond checkpoint inhibitors. However, many of the new drugs either do not progress from phase I-II clinical trials or even fail in late-phase trials. We have identified at least five areas in the development of promising IO treatments that should be redefined for more efficient designs and accelerated approvals. Here we review those critical aspects of IO drug development that could be optimized for more successful outcome rates in all cancer types. It is important to focus our efforts on the mechanisms of action, types of response and adverse events of these novel agents. The use of appropriate clinical trial designs with robust biomarkers of response and surrogate endpoints will undoubtedly facilitate the development and subsequent approval of these drugs. Further research is also needed to establish biomarker-driven strategies to select which patients may benefit from immunotherapy and identify potential mechanisms of resistance.

scholarly journals Delivery of cancer therapies by synthetic and bio-inspired nanovectors

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Tina Briolay ◽  
Tacien Petithomme ◽  
Morgane Fouet ◽  
Nelly Nguyen-Pham ◽  
Christophe Blanquart ◽  
...  
Keyword(s):  
Recent Literature ◽  
Biological Properties ◽  
Engineered Nanoparticles ◽  
Oncolytic Viruses ◽  
Cancer Therapies ◽  
Different Types ◽  
Therapeutic Molecules ◽  
Tumor Specificity ◽  
Smart Drug ◽  
Smart Drug Delivery

Abstract Background As a complement to the clinical development of new anticancer molecules, innovations in therapeutic vectorization aim at solving issues related to tumor specificity and associated toxicities. Nanomedicine is a rapidly evolving field that offers various solutions to increase clinical efficacy and safety. Main Here are presented the recent advances for different types of nanovectors of chemical and biological nature, to identify the best suited for translational research projects. These nanovectors include different types of chemically engineered nanoparticles that now come in many different flavors of ‘smart’ drug delivery systems. Alternatives with enhanced biocompatibility and a better adaptability to new types of therapeutic molecules are the cell-derived extracellular vesicles and micro-organism-derived oncolytic viruses, virus-like particles and bacterial minicells. In the first part of the review, we describe their main physical, chemical and biological properties and their potential for personalized modifications. The second part focuses on presenting the recent literature on the use of the different families of nanovectors to deliver anticancer molecules for chemotherapy, radiotherapy, nucleic acid-based therapy, modulation of the tumor microenvironment and immunotherapy. Conclusion This review will help the readers to better appreciate the complexity of available nanovectors and to identify the most fitting “type” for efficient and specific delivery of diverse anticancer therapies.

scholarly journals Microenvironmental variables need to effect intrinsic phenotypic parameters of cancer stem cells to affect tumourigenicity

2013 ◽  
Author(s):  
Jacob G Scott ◽  
Prakash Chinnaiyan ◽  
Alexander ARA Anderson ◽  
Anita Hjelmeland ◽  
David Basanta
Keyword(s):  
Stem Cell ◽  
Therapeutic Strategy ◽  
Tissue Growth ◽  
Genetic Mutations ◽  
Phenotypic Traits ◽  
Cell Behaviour ◽  
New Information ◽  
Cancer Initiation ◽  
Cancer Types ◽  
Direct Feedback

Since the discovery of tumour initiating cells (TICs) in solid tumours, studies focussing on their role in cancer initiation and progression have abounded. The biological interrogation of these cells continues to yield volumes of information on their pro-tumourigenic behaviour, but actionable generalised conclusions have been scarce. Further, new information suggesting a dependence of tumour composition and growth on the microenvironment has yet to be studied theoretically. To address this point, we created a hybrid, discrete/continuous computational cellular automaton model of a generalised stem-cell driven tissue with a simple microenvironment. Using the model we explored the phenotypic traits inherent to the tumour initiating cells and the effect of the microenvironment on tissue growth. We identify the regions in phenotype parameter space where TICs are able to cause a disruption in homeostasis, leading to tissue overgrowth and tumour maintenance. As our parameters and model are non- specific, they could apply to any tissue TIC and do not assume specific genetic mutations. Targeting these phenotypic traits could represent a generalizable therapeutic strategy across cancer types. Further, we find that the microenvironmental variable does not strongly effect the outcomes, suggesting a need for direct feedback from the microenvironment onto stem-cell behaviour in future modelling endeavours.

15. Democracies, Democratization, and Authoritarian Regimes

2020 ◽  
pp. 336-362
Author(s):  
Peter Ferdinand
Keyword(s):  
Economic Development ◽  
Recent Literature ◽  
Authoritarian Regimes ◽  
Short Term ◽  
National Crisis ◽  
Different Types ◽  
Types Of Democracy ◽  
The Face ◽  
Long Term Trends

This chapter focuses on democracies, democratization, and authoritarian regimes. It first considers the two main approaches to analysing the global rise of democracy over the last thirty years: first, long-term trends of modernization, and more specifically economic development, that create preconditions for democracy and opportunities for democratic entrepreneurs; and second, the sequences of more short-term events and actions of key actors at moments of national crisis that have precipitated a democratic transition — also known as ‘transitology’. The chapter proceeds by discussing the different types of democracy and the strategies used to measure democracy. It also reviews the more recent literature on authoritarian systems and why they persist. Finally, it examines the challenges that confront democracy in the face of authoritarian revival.

scholarly journals A Novel Framework for Selecting Informative Meteorological Stations Using Monte Carlo Feature Selection (MCFS) Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Rizwan Niaz ◽  
Ibrahim M. Almanjahie ◽  
Zulfiqar Ali ◽  
Muhammad Faisal ◽  
Ijaz Hussain
Keyword(s):  
Monte Carlo ◽  
Feature Selection ◽  
Significant Role ◽  
Meteorological Data ◽  
Accurate Estimation ◽  
Relative Importance ◽  
Drought Prediction ◽  
Monte Carlo Feature Selection ◽  
Remaining Time ◽  
Scale Data

Spatial distribution of meteorological stations has a significant role in hydrological research. The meteorological data play a significant role in drought monitoring; in this regard, accurate and suitable provision of meteorological stations is becoming crucial to improve and strengthen the skill of drought prediction. In this perspective, the choice of meteorological stations in a specific region has substantial importance for accurate estimation and continuous monitoring of drought hazards at the regional level. However, installation and data mining on a large number of meteorological stations require high cost and resources. Therefore, it is necessary to rank and find dependencies among existing meteorological stations in a particular region for further climatological analysis and reanalysis of databases. In this paper, the Monte Carlo feature selection and interdependency discovery (MCFS-ID) algorithm-based framework is proposed to identify the important meteorological station in a particular region. We applied the proposed framework on 12 meteorological stations situated in varying climatological regions of Punjab (Pakistan). We employed the drought index SPTI on 1-, 3-, 6-, 9-, 12-, 24-, and 48-month time-scale data to find the interdependencies among meteorological stations at various locations. We found that Sialkot has significance regional importance for studying SPTI-3, SPTI-6, and SPTI-48 indices. This regional importance is based on scores of relative importance (RI); for example, the RI values for SPTI-3, SPTI-6, and SPTI-48 indices are 0.1570, 0.1080, and 0.0270, respectively. Furthermore, the Jhelum station has more relative importance (RI = 0.1410 and 0.1030) for SPTI-1 and SPTI-9 indices, while varying concentration behaviour is observed in the remaining time scales.

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