scholarly journals Complex Age- and Cancer-Related Changes in Human Blood Transcriptome—Implications for Pan-Cancer Diagnostics

2021 ◽  
Vol 12 ◽  
Author(s):  
Fei Qi ◽  
Fan Gao ◽  
Ye Cai ◽  
Xueer Han ◽  
Yao Qi ◽  
...  
Keyword(s):  
Cancer Detection ◽  
Liquid Biopsy ◽  
Early Stage ◽  
Cancer Diagnostics ◽  
Superior Performance ◽  
Protein Coding ◽  
Whole Transcriptome Analysis ◽  
Molecular Components ◽  
Pan Cancer ◽  
Blood Transcriptome

Early cancer detection is the key to a positive clinical outcome. While a number of early diagnostics methods exist in clinics today, they tend to be invasive and limited to a few cancer types. Thus, a clear need exists for non-invasive diagnostics methods that can be used to detect the presence of cancer of any type. Liquid biopsy based on analysis of molecular components of peripheral blood has shown significant promise in such pan-cancer diagnostics; however, existing methods based on this approach require improvements, especially in sensitivity of early-stage cancer detection. The improvement would likely require diagnostics assays based on multiple different types of biomarkers and, thus, calls for identification of novel types of cancer-related biomarkers that can be used in liquid biopsy. Whole-blood transcriptome, especially its non-coding component, represents an obvious yet under-explored biomarker for pan-cancer detection. In this study, we show that whole transcriptome analysis using RNA-seq could indeed serve as a viable biomarker for pan-cancer detection. Furthermore, a class of long non-coding (lnc) RNAs, very long intergenic non-coding (vlinc) RNAs, demonstrated superior performance compared with protein-coding mRNAs. Finally, we show that age and presence of non-blood cancers change transcriptome in similar, yet not identical, directions and explore implications of this observation for pan-cancer diagnostics.

scholarly journals Machine Learning Protocols in Early Cancer Detection Based on Liquid Biopsy: A Survey

Life ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 638
Author(s):  
Linjing Liu ◽  
Xingjian Chen ◽  
Olutomilayo Olayemi Petinrin ◽  
Weitong Zhang ◽  
Saifur Rahaman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cancer Detection ◽  
Liquid Biopsy ◽  
Cancer Diagnostics ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Cancer Subtype ◽  
Tumor Origin ◽  
Complex Relationships ◽  
Learning Protocols

With the advances of liquid biopsy technology, there is increasing evidence that body fluid such as blood, urine, and saliva could harbor the potential biomarkers associated with tumor origin. Traditional correlation analysis methods are no longer sufficient to capture the high-resolution complex relationships between biomarkers and cancer subtype heterogeneity. To address the challenge, researchers proposed machine learning techniques with liquid biopsy data to explore the essence of tumor origin together. In this survey, we review the machine learning protocols and provide corresponding code demos for the approaches mentioned. We discuss algorithmic principles and frameworks extensively developed to reveal cancer mechanisms and consider the future prospects in biomarker exploration and cancer diagnostics.

An ultrasensitive method for noninvasive pan-cancer early detection based on targeted methylation sequencing of cell-free DNA.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 10544-10544
Author(s):  
Tiancheng Han ◽  
Yuanyuan Hong ◽  
Pei Zhihua ◽  
Song Xiaofeng ◽  
Jianing Yu ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Cancer Detection ◽  
Real World ◽  
Early Stage ◽  
Validation Dataset ◽  
Cpg Sites ◽  
Free Dna ◽  
Cancer Types ◽  
Discovery Dataset ◽  
Pan Cancer

10544 Background: Screening the biomarkers from the cell-free DNA (cfDNA) of peripheral blood is a non-invasive and promising method for cancer diagnosis. Among diverse types of biomarkers, epigenetic biomarkers have been reported to be one of the most promising ones. Epigenetic modifications are widespread on the human genome and generally have strong signals due to the similar methylation patterns shared by adjacent CpG sites. Although some epigenetic diagnostic methods have been developed based on cfDNAs, few of them could be applied to pan-cancer and their sensitivities are barely satisfactory for early cancer detection. Methods: Targeted methylation sequencing was performed using our in-house-designed panel targeting regions with abundant cancer-specific methylation CpGs. The cfDNA samples from 80 healthy individuals and 549 cancer patients of 14 cancer types were separately sequenced. The dataset was randomly split into one discovery dataset and one validation dataset. Moreover, cfDNA samples from four cancer patients were diluted with the healthy cfDNAs to generate 12 in vitro simulated samples with low circulating tumor DNA (ctDNA) fraction. Additionally, DNAs extracted from 130 unmatched tumor formalin fixation and paraffin embedding (FFPE) samples of 10 cancer types were sequenced to screen the diagnostic biomarkers. Adjacent CpG sites were first merged into methylation-correlated blocks (MCB) according to their correlations of methylation levels in tumor DNAs. The MCBs with higher methylation levels in tumor DNAs than that of healthy cfDNAs (from the discovery dataset) were defined as our hypermethylation biomarkers. For each cfDNA sample, a hypermethylation score (HM-score) was computed to measure the overall methylation level difference of selected biomarkers. The performance of our method was evaluated with the real-world dataset, while the limit of detection was estimated using the simulated low-ctDNA samples. Results: Our model based on 37 hypermethylation MCB biomarkers achieved an area under the curve (AUC) of 0.89 and 0.86 in the real-world pan-cancer discovery and validation cfDNA datasets, respectively. Furthermore, the overall specificity and sensitivity are 100% and 76.19% in the discovery dataset, and 96.67% and 72.86% in the validation dataset. In the validation dataset, 28/40 (70%) of early-stage colorectal cancer patients and 10/20 (50%) of non-small-cell lung cancer patients were successfully diagnosed. Additionally, all the simulated samples with theoretical ctDNA factions over 0.5% were predicted as diseased, demonstrating the ability of our method to detect tumor signals at early stages. Conclusions: Our cfDNA-based epigenetic method outperforms currently available methods in various cancer types, and is promising to be applied to early-stage cancer detection and samples with low ctDNA fractions.

Gene expression analysis in modeling Parkinson’s disease

2020 ◽  
pp. 103-104
Author(s):  
М.М. Руденок ◽  
А.Х. Алиева ◽  
А.А. Колачева ◽  
М.В. Угрюмов ◽  
П.А. Сломинский ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Stage ◽  
Systemic Disease ◽  
Molecular Genetic ◽  
Presymptomatic Stage ◽  
Whole Transcriptome Analysis ◽  
Whole Transcriptome ◽  
The Brain ◽  
Transcriptome Level

Несмотря на очевидный прогресс, достигнутый в изучении молекулярно-генетических факторов и механизмов патогенеза болезни Паркинсона (БП), в настоящее время стало ясно, что нарушения в структуре ДНК не описывают весь спектр патологических изменений, наблюдаемых при развитии заболевания. В настоящее время показано, что существенное влияние на патогенез БП могут оказывать изменения на уровне транскриптома. В работе были использованы мышиные модели досимптомной стадии БП, поздней досимптомной и ранней симптомной (РСС) стадиями БП. Для полнотранскриптомного анализа пулов РНК тканей черной субстанции и стриатума мозга мышей использовались микрочипы MouseRef-8 v2.0 Expression BeadChip Kit («Illumina», США). Полученные данные указывают на последовательное вовлечение транскриптома в патогенез БП, а также на то, что изменения на транскриптомном уровне процессов транспорта и митохондриального биогенеза могут играть важную роль в нейродегенерации при БП уже на самых ранних этапах. Parkinson’s disease (PD) is a complex systemic disease, mainly associated with the death of dopaminergic neurons. Despite the obvious progress made in the study of molecular genetic factors and mechanisms of PD pathogenesis, it has now become clear that violations in the DNA structure do not describe the entire spectrum of pathological changes observed during the development of the disease. It has now been shown that changes at the transcriptome level can have a significant effect on the pathogenesis of PD. The authors used models of the presymptomatic stage of PD with mice decapitation after 6 hours (6 h-PSS), presymptomatic stage with decapitation after 24 hours (24 h-PSS), advanced presymptomatic (Adv-PSS) and early symptomatic (ESS) stages of PD. For whole transcriptome analysis of RNA pools of the substantia nigra and mouse striatum, the MouseRef-8 v2.0 Expression BeadChip Kit microchips (Illumina, USA) were used. As a result of the analysis of whole transcriptome data, it was shown that, there are a greater number of statistically significant changes in the tissues of the brain and peripheral blood of mice with Adv-PSS and ESS models of PD compared to 6 h-PSS and 24 h-PSS models. In general, the obtained data indicate the sequential involvement of the transcriptome in the pathogenesis of PD, as well as the fact that changes at the transcriptome level of the processes of transport and mitochondrial biogenesis can play an important role in neurodegeneration in PD at an early stage.

Green synthesis of nanomaterials for early lung cancer detection

2018 ◽  
pp. 1-1
Keyword(s):  
Lung Cancer ◽  
Green Synthesis ◽  
Cancer Detection ◽  
World Wide ◽  
Early Stage ◽  
Age Groups ◽  
Early Lung Cancer ◽  
Lung Cancer Detection ◽  
Synthesis Of Nanomaterials

Lung cancer is the foremost cause of cancer-related deaths world-wide [1]. It affects 100,000 Americans of the smoking population every year of all age groups, particularly those above 50 years of the smoking population [2]. In India, 51,000 lung cancer deaths were reported in 2012, which include 41,000 men and 10,000 women [3]. It is the leading cause of cancer deaths in men; however, in women, it ranked ninth among all cancerous deaths [4]. It is possible to detect the lung cancer at a very early stage, providing a much higher chance of survival for the patients.

scholarly journals DEEPGENTM—A Novel Variant Calling Assay for Low Frequency Variants

Genes ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 507
Author(s):  
Bernd Timo Hermann ◽  
Sebastian Pfeil ◽  
Nicole Groenke ◽  
Samuel Schaible ◽  
Robert Kunze ◽  
...  
Keyword(s):  
Cancer Detection ◽  
Genetic Variants ◽  
Liquid Biopsy ◽  
Hot Spot ◽  
Treatment Success ◽  
Low Frequency ◽  
Variant Calling ◽  
Subsequent Treatment ◽  
Precision Oncology ◽  
Orthogonal Comparison

Detection of genetic variants in clinically relevant genomic hot-spot regions has become a promising application of next-generation sequencing technology in precision oncology. Effective personalized diagnostics requires the detection of variants with often very low frequencies. This can be achieved by targeted, short-read sequencing that provides high sequencing depths. However, rare genetic variants can contain crucial information for early cancer detection and subsequent treatment success, an inevitable level of background noise usually limits the accuracy of low frequency variant calling assays. To address this challenge, we developed DEEPGENTM, a variant calling assay intended for the detection of low frequency variants within liquid biopsy samples. We processed reference samples with validated mutations of known frequencies (0%–0.5%) to determine DEEPGENTM’s performance and minimal input requirements. Our findings confirm DEEPGENTM’s effectiveness in discriminating between signal and noise down to 0.09% variant allele frequency and an LOD(90) at 0.18%. A superior sensitivity was also confirmed by orthogonal comparison to a commercially available liquid biopsy-based assay for cancer detection.

scholarly journals A COMPARISON OF STRUCTURED LIGHT SCANNING AND PHOTOGRAMMETRY FOR THE DIGITISATION OF PHYSICAL PROTOTYPES

2021 ◽  
Vol 1 ◽  
pp. 11-20
Author(s):  
Owen Freeman Gebler ◽  
Mark Goudswaard ◽  
Ben Hicks ◽  
David Jones ◽  
Aydin Nassehi ◽  
...  
Keyword(s):  
Early Stage ◽  
Structured Light ◽  
Superior Performance ◽  
Specific Design ◽  
Model Quality ◽  
Stage Design ◽  
Design Changes ◽  
Structured Light Scanning ◽  
Early Stage Design

AbstractPhysical prototyping during early stage design typically represents an iterative process. Commonly, a single prototype will be used throughout the process, with its form being modified as the design evolves. If the form of the prototype is not captured as each iteration occurs understanding how specific design changes impact upon the satisfaction of requirements is challenging, particularly retrospectively.In this paper two different systems for digitising physical artefacts, structured light scanning (SLS) and photogrammetry (PG), are investigated as means for capturing iterations of physical prototypes. First, a series of test artefacts are presented and procedures for operating each system are developed. Next, artefacts are digitised using both SLS and PG and resulting models are compared against a master model of each artefact. Results indicate that both systems are able to reconstruct the majority of each artefact's geometry within 0.1mm of the master, however, overall SLS demonstrated superior performance, both in terms of completion time and model quality. Additionally, the quality of PG models was far more influenced by the effort and expertise of the user compared to SLS.

Sign in / Sign up

Export Citation Format

Share Document