Copy Number Variation Pattern for Discriminating MACROD2 States of Colorectal Cancer Subtypes

Mutation-derived, genomic instability-associated lncRNAs are prognostic markers in gliomas

Background

Gliomas are the most commonly-detected malignant tumors of the brain. They contain abundant long non-coding RNAs (lncRNAs), which are valuable cancer biomarkers. LncRNAs may be involved in genomic instability; however, their specific role and mechanism in gliomas remains unclear. LncRNAs that are related to genomic instability have not been reported in gliomas.

Methods

The transcriptome data from The Cancer Genome Atlas (TCGA) database were analyzed. The co-expression network of genomic instability-related lncRNAs and mRNA was established, and the model of genomic instability-related lncRNA was identified by univariate Cox regression and LASSO analyses. Based on the median risk score obtained in the training set, we divided the samples into high-risk and low-risk groups and proved the survival prediction ability of genomic instability-related lncRNA signatures. The results were verified in the external data set. Finally, a real-time quantitative polymerase chain reaction assay was performed to validate the signature.

Results

The signatures of 17 lncRNAs (LINC01579, AL022344.1, AC025171.5, LINC01116, MIR155HG, AC131097.3, LINC00906, CYTOR, AC015540.1, SLC25A21.AS1, H19, AL133415.1, SNHG18, FOXD3.AS1, LINC02593, AL354919.2 and CRNDE) related to genomic instability were identified. In the internal data set and Gene Expression Omnibus (GEO) external data set, the low-risk group showed better survival than the high-risk group (P < 0.001). In addition, this feature was identified as an independent risk factor, showing its independent prognostic value with different clinical stratifications. The majority of patients in the low-risk group had isocitrate dehydrogenase 1 (IDH1) mutations. The expression levels of these lncRNAs were significantly higher in glioblastoma cell lines than in normal cells.

Conclusions

Our study shows that the signature of 17 lncRNAs related to genomic instability has prognostic value for gliomas and could provide a potential therapeutic method for glioblastoma.

Integrated Workflow for the Label-Free Isolation and Genomic Analysis of Single Circulating Tumor Cells in Pancreatic Cancer

As pancreatic cancer is the third deadliest cancer in the U.S., the ability to study genetic alterations is necessary to provide further insight into potentially targetable regions for cancer treatment. Circulating tumor cells (CTCs) represent an especially aggressive subset of cancer cells, capable of causing metastasis and progressing the disease. Here, we present the Labyrinth-DEPArray pipeline for the isolation and analysis of single CTCs. Established cell lines, patient-derived CTC cell lines and freshly isolated CTCs were recovered and sequenced to reveal single-cell copy number variations (CNVs). The resulting CNV profiles of established cell lines showed concordance with previously reported data and highlight several gains and losses of cancer-related genes such as FGFR3 and GNAS. The novel sequencing of patient-derived CTC cell lines showed gains in chromosome 8q, 10q and 17q across both CTC cell lines. The pipeline was used to process and isolate single cells from a metastatic pancreatic cancer patient revealing a gain of chromosome 1q and a loss of chromosome 5q. Overall, the Labyrinth-DEPArray pipeline offers a validated workflow combining the benefits of antigen-free CTC isolation with single cell genomic analysis.

Development of a Genomic Instability-Derived lncRNAs-Based Risk Signature as a Predictor of Prognosis for Endometrial Cancer

Endometrial cancer (EC) ranks fourth in the incidence rate among the most frequent gynaecological malignancies reported in the developed countries. Approximately 280,000 endometrial cancer cases are reported worldwide every year. Genomic instability and mutation are some of the favourable characteristics of human malignancies such as endometrial cancer. Studies have established that the majority of genomic mutations in human malignancies are found in the chromosomal regions that do not code for proteins. In addition, the majority of transcriptional products of these mutations are long non-coding RNAs (lncRNAs). In this study, 78 lncRNA genes were found on the basis of their mutation counts. Then, these lncRNAs were investigated to determine their relationship with genomic instability through hierarchical cluster analysis, mutation analysis, and differential analysis of driving genes responsible for genomic instability. The prognostic value of these lncRNAs was also assessed in patients with EC, and a risk factor score formula composed of 15 lncRNAs was constructed. We then identified this formula as genome instability-derived lncRNA-based gene signature (GILncSig), which stratified patients into high- and low-risk groups with significantly different outcome. And GILncSig was further validated in multiple independent patient cohorts as a prognostic factor of other clinicopathological features, such as stage, grade, overall survival rate. We observed that a high-risk score is often associated with an unfavourable prognosis in patients with EC.

Sparse modelling of cancer patients' survival based on genomic copy number alterations

Copy number alterations (CNA) are structural variation in the genome, in which some regions exhibit more or less than the normal two chromosomal copies. This genomic CNA profile provides critical information in tumour progression and is therefore informative for patients' survival. It is currently a statistical challenge to model patients' survival using their genomic CNA profiles while at the same time identify regions in the genome that are associated with patients' survival. Some methods have been proposed, including Cox proportional hazard (PH) model with ridge, lasso, or elastic net penalties. However, these methods do not take the general dependencies between genomic regions into account and produce results that are difficult to interpret. In this paper, we extend the elastic net penalty by introducing additional penalty that takes into account general dependencies between genomic regions. This new model produces smooth parameter estimates while simultaneously performs variable selection via sparse solution. The results indicate that the proposed method shows a better prediction performance than other models in our simulation study, while enabling us to investigate regions in the genome that are associated with the patients' survival with sensible interpretation. We illustrate the method using a real dataset from a lung cancer cohort and simulated data.

DriverFuse: An R package for analysis of next-generation sequencing datasets to identify cancer driver fusion genes

We developed the DriverFuse package to integrate orthogonal data types such as Structural Variants (SV) and Copy Number Variations (CNV) to characterize fusion genes in cancer datasets. A fusion gene is reported as a driver or passenger fusion gene, based on mapping SV and CNV profiles. DriverFuse generates a fusion plot of fusion genes with their mapping SV, CNV profile, domain architecture and classification of its role in cancer. The analysis facilitates discrimination of driver fusions from passenger fusions. To demonstrate the utility of DriverFuse, we analyzed two datasets, one each for CCLE (Cancer Cell Line Encyclopedia) for lung cancer and HCC1395BL for breast cancer. The analysis validates the driver fusion genes that are already reported for the datasets. Thus, DriverFuse is a valuable tool for studying the driver fusion genes in cancers, enabling the identification of recurrent complex rearrangements that provide intuitive insights into disease driver events.

Predicting RNA 5-Methylcytosine Sites by Using Essential Sequence Features and Distributions

Methylation is one of the most common and considerable modifications in biological systems mediated by multiple enzymes. Recent studies have shown that methylation has been widely identified in different RNA molecules. RNA methylation modifications have various kinds, such as 5-methylcytosine (m5C). However, for individual methylation sites, their functions still remain to be elucidated. Testing of all methylation sites relies heavily on high-throughput sequencing technology, which is expensive and labor consuming. Thus, computational prediction approaches could serve as a substitute. In this study, multiple machine learning models were used to predict possible RNA m5C sites on the basis of mRNA sequences in human and mouse. Each site was represented by several features derived from $k$ -mers of an RNA subsequence containing such site as center. The powerful max-relevance and min-redundancy (mRMR) feature selection method was employed to analyse these features. The outcome feature list was fed into incremental feature selection method, incorporating four classification algorithms, to build efficient models. Furthermore, the sites related to features used in the models were also investigated.

Artificial intelligence: A powerful paradigm for scientific research

Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.

Identification of Microbiota Biomarkers With Orthologous Gene Annotation for Type 2 Diabetes

Type 2 diabetes (T2D) is a systematic chronic metabolic condition with abnormal sugar metabolism dysfunction, and its complications are the most harmful to human beings and may be life-threatening after long-term durations. Considering the high incidence and severity at late stage, researchers have been focusing on the identification of specific biomarkers and potential drug targets for T2D at the genomic, epigenomic, and transcriptomic levels. Microbes participate in the pathogenesis of multiple metabolic diseases including diabetes. However, the related studies are still non-systematic and lack the functional exploration on identified microbes. To fill this gap between gut microbiome and diabetes study, we first introduced eggNOG database and KEGG ORTHOLOGY (KO) database for orthologous (protein/gene) annotation of microbiota. Two datasets with these annotations were employed, which were analyzed by multiple machine-learning models for identifying significant microbiota biomarkers of T2D. The powerful feature selection method, Max-Relevance and Min-Redundancy (mRMR), was first applied to the datasets, resulting in a feature list for each dataset. Then, the list was fed into the incremental feature selection (IFS), incorporating support vector machine (SVM) as the classification algorithm, to extract essential annotations and build efficient classifiers. This study not only revealed potential pathological factors for diabetes at the microbiome level but also provided us new candidates for drug development against diabetes.

Identifying COVID-19-Specific Transcriptomic Biomarkers with Machine Learning Methods

COVID-19, a severe respiratory disease caused by a new type of coronavirus SARS-CoV-2, has been spreading all over the world. Patients infected with SARS-CoV-2 may have no pathogenic symptoms, i.e., presymptomatic patients and asymptomatic patients. Both patients could further spread the virus to other susceptible people, thereby making the control of COVID-19 difficult. The two major challenges for COVID-19 diagnosis at present are as follows: (1) patients could share similar symptoms with other respiratory infections, and (2) patients may not have any symptoms but could still spread the virus. Therefore, new biomarkers at different omics levels are required for the large-scale screening and diagnosis of COVID-19. Although some initial analyses could identify a group of candidate gene biomarkers for COVID-19, the previous work still could not identify biomarkers capable for clinical use in COVID-19, which requires disease-specific diagnosis compared with other multiple infectious diseases. As an extension of the previous study, optimized machine learning models were applied in the present study to identify some specific qualitative host biomarkers associated with COVID-19 infection on the basis of a publicly released transcriptomic dataset, which included healthy controls and patients with bacterial infection, influenza, COVID-19, and other kinds of coronavirus. This dataset was first analysed by Boruta, Max-Relevance and Min-Redundancy feature selection methods one by one, resulting in a feature list. This list was fed into the incremental feature selection method, incorporating one of the classification algorithms to extract essential biomarkers and build efficient classifiers and classification rules. The capacity of these findings to distinguish COVID-19 with other similar respiratory infectious diseases at the transcriptomic level was also validated, which may improve the efficacy and accuracy of COVID-19 diagnosis.

Identification of the Prognostic Significance of Somatic Mutation-Derived LncRNA Signatures of Genomic Instability in Lung Adenocarcinoma

Background: Lung adenocarcinoma (LUAD) is a highly heterogeneous tumor with substantial somatic mutations and genome instability, which are emerging hallmarks of cancer. Long non-coding RNAs (lncRNAs) are promising cancer biomarkers that are reportedly involved in genomic instability. However, the identification of genome instability-related lncRNAs (GInLncRNAs) and their clinical significance has not been investigated in LUAD.

Methods: We determined GInLncRNAs by combining somatic mutation and transcriptome data of 457 patients with LUAD and probed their potential function using co-expression network and Gene Ontology (GO) enrichment analyses. We then filtered GInLncRNAs by Cox regression and LASSO regression to construct a genome instability-related lncRNA signature (GInLncSig). We subsequently evaluated GInLncSig using correlation analyses with mutations, external validation, model comparisons, independent prognostic significance analyses, and clinical stratification analyses. Finally, we established a nomogram for prognosis prediction in patients with LUAD and validated it in the testing set and the entire TCGA dataset.

Results: We identified 161 GInLncRNAs, of which seven were screened to develop a prognostic GInLncSig model (LINC01133, LINC01116, LINC01671, FAM83A-AS1, PLAC4, MIR223HG, and AL590226.1). GInLncSig independently predicted the overall survival of patients with LUAD and displayed an improved performance compared to other similar signatures. Furthermore, GInLncSig was related to somatic mutation patterns, suggesting its ability to reflect genome instability in LUAD. Finally, a nomogram comprising the GInLncSig and tumor stage exhibited improved robustness and clinical practicability for predicting patient prognosis.

Conclusion: Our study identified a signature for prognostic prediction in LUAD comprising seven lncRNAs associated with genome instability, which may provide a useful indicator for clinical stratification management and treatment decisions for patients with LUAD.

Uncovering the Invisible: Mono-ADP-ribosylation Moved into the Spotlight

Adenosine diphosphate (ADP)-ribosylation is a nicotinamide adenine dinucleotide (NAD+)-dependent post-translational modification that is found on proteins as well as on nucleic acids. While ARTD1/PARP1-mediated poly-ADP-ribosylation has extensively been studied in the past 60 years, comparably little is known about the physiological function of mono-ADP-ribosylation and the enzymes involved in its turnover. Promising technological advances have enabled the development of innovative tools to detect NAD+ and NAD+/NADH (H for hydrogen) ratios as well as ADP-ribosylation. These tools have significantly enhanced our current understanding of how intracellular NAD dynamics contribute to the regulation of ADP-ribosylation as well as to how mono-ADP-ribosylation integrates into various cellular processes. Here, we discuss the recent technological advances, as well as associated new biological findings and concepts.

Identification of Protein Subcellular Localization With Network and Functional Embeddings

The functions of proteins are mainly determined by their subcellular localizations in cells. Currently, many computational methods for predicting the subcellular localization of proteins have been proposed. However, these methods require further improvement, especially when used in protein representations. In this study, we present an embedding-based method for predicting the subcellular localization of proteins. We first learn the functional embeddings of KEGG/GO terms, which are further used in representing proteins. Then, we characterize the network embeddings of proteins on a protein–protein network. The functional and network embeddings are combined as novel representations of protein locations for the construction of the final classification model. In our collected benchmark dataset with 4,861 proteins from 16 locations, the best model shows a Matthews correlation coefficient of 0.872 and is thus superior to multiple conventional methods.

Identifying Transcriptomic Signatures and Rules for SARS-CoV-2 Infection

The world-wide Coronavirus Disease 2019 (COVID-19) pandemic was triggered by the widespread of a new strain of coronavirus named as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Multiple studies on the pathogenesis of SARS-CoV-2 have been conducted immediately after the spread of the disease. However, the molecular pathogenesis of the virus and related diseases has still not been fully revealed. In this study, we attempted to identify new transcriptomic signatures as candidate diagnostic models for clinical testing or as therapeutic targets for vaccine design. Using the recently reported transcriptomics data of upper airway tissue with acute respiratory illnesses, we integrated multiple machine learning methods to identify effective qualitative biomarkers and quantitative rules for the distinction of SARS-CoV-2 infection from other infectious diseases. The transcriptomics data was first analyzed by Boruta so that important features were selected, which were further evaluated by the minimum redundancy maximum relevance method. A feature list was produced. This list was fed into the incremental feature selection, incorporating some classification algorithms, to extract qualitative biomarker genes and construct quantitative rules. Also, an efficient classifier was built to identify patients infected with SARS-COV-2. The findings reported in this study may help in revealing the potential pathogenic mechanisms of COVID-19 and finding new targets for vaccine design.

RET Copy Number Alteration in Medullary Thyroid Cancer Is a Rare Event Correlated with RET Somatic Mutations and High Allelic Frequency

Copy number variations (CNV) of the RET gene have been described in 30% of Medullary Thyroid Cancer (MTC), but no information is available about their role in this tumor. This study was designed to clarify RET gene CNV prevalence and their potential role in MTC development. RET gene CNV were analyzed in 158 sporadic MTC cases using the ION Reporter Software (i.e., in silico analysis) while the multiplex ligation-dependent probe amplification assay (i.e., in vitro analysis) technique was performed in 78 MTC cases. We identified three categories of RET ploidy: 137 in 158 (86.7%) cases were diploid and 21 in 158 (13.3%) were aneuploid. Among the aneuploid cases, five out of 21 (23.8%) showed an allelic deletion while 16 out of 21 (76.2%) had an allelic amplification. The prevalence of amplified or deleted RET gene cases (aneuploid) was higher in RET positive tumors. Aneuploid cases also showed a higher allelic frequency of the RET driver mutation. The prevalence of patients with metastatic disease was higher in the group of aneuploid cases while the higher prevalence of disease-free patients was observed in diploid tumors. A statistically significant difference was found when comparing the ploidy status and mortality. RET gene CNVs are rare events in sporadic MTC and are associated with RET somatic mutation, suggesting that they could not be a driver mechanism of tumoral transformation per se. Finally, we found a positive correlation between RET gene CNV and a worse clinical outcome.

Chromosome Abnormalities: New Insights into Their Clinical Significance in Cancer

Chromosomal abnormalities, consisting of numerical and structural chromosome abnormalities, are a common characteristic of cancer. Numerical chromosome abnormalities, mainly including aneuploidy and chromosome instability, are caused by chromosome segregation errors in mitosis, whereas structural chromosome abnormalities are a consequence of DNA damage and comprise focal/arm-level chromosome gain or loss. Recent advances have started to unveil the mechanisms by which chromosomal abnormalities can facilitate tumorigenesis and change the cellular fitness and the expression or function of RNAs and proteins. Accumulating evidence suggests that chromosome abnormalities represent a genomic signature that is linked to cancer prognosis and reaction to chemotherapy and immunotherapy. In this review, we discuss the most recent findings on the role of chromosome abnormalities in tumorigenesis and cancer progression, with a particular emphasis on how aneuploidy and chromosome instability influence cancer therapy and prognosis. We also highlight the distribution and clinical application of the structural chromosome abnormalities in various cancer types. A better understanding of the role of chromosome abnormalities will be beneficial to the development of precision oncology and suggest future directions for the field.