Transcriptomics in cancer diagnostics: developments in technology, clinical research and commercialization

Cancer pharmacoinformatics: Databases and analytical tools

Cancer is a subject of extensive investigation, and the utilization of omics technology has resulted in the generation of substantial volumes of big data in cancer research. Numerous databases are being developed to manage and organize this data effectively. These databases encompass various domains such as genomics, transcriptomics, proteomics, metabolomics, immunology, and drug discovery. The application of computational tools into various core components of pharmaceutical sciences constitutes "Pharmacoinformatics", an emerging paradigm in rational drug discovery. The three major features of pharmacoinformatics include (i) Structure modelling of putative drugs and targets, (ii) Compilation of databases and analysis using statistical approaches, and (iii) Employing artificial intelligence/machine learning algorithms for the discovery of novel therapeutic molecules. The development, updating, and analysis of databases using statistical approaches play a pivotal role in pharmacoinformatics. Multiple software tools are associated with oncoinformatics research. This review catalogs the databases and computational tools related to cancer drug discovery and highlights their potential implications in the pharmacoinformatics of cancer.

Machine learning and related approaches in transcriptomics

Data acquisition for transcriptomic studies used to be the bottleneck in the transcriptomic analytical pipeline. However, recent developments in transcriptome profiling technologies have increased researchers' ability to obtain data, resulting in a shift in focus to data analysis. Incorporating machine learning to traditional analytical methods allows the possibility of handling larger volumes of complex data more efficiently. Many bioinformaticians, especially those unfamiliar with ML in the study of human transcriptomics and complex biological systems, face a significant barrier stemming from their limited awareness of the current landscape of ML utilisation in this field. To address this gap, this review endeavours to introduce those individuals to the general types of ML, followed by a comprehensive range of more specific techniques, demonstrated through examples of their incorporation into analytical pipelines for human transcriptome investigations. Important computational aspects such as data pre-processing, task formulation, results (performance of ML models), and validation methods are encompassed. In hope of better practical relevance, there is a strong focus on studies published within the last five years, almost exclusively examining human transcriptomes, with outcomes compared with standard non-ML tools.

Tetrastigma hemsleyanum suppresses neuroinflammation in febrile seizures rats via regulating PKC-δ/caspase-1 signaling pathway

ETHNOPHARMACOLOGICAL RELEVANCE

Tetrastigma hemsleyanum Diels et Gilg (T. hemsleyanum, Sanyeqing) has been used in the prevention and treatment of repetitive Febrile seizures (FS) over the centuries in China.

AIM OF THE STUDY

T. hemsleyanum exerts wide pharmacological action, which has been widely used for treating various diseases, including infantile febrile seizure. However, the systematic study on this herb's material basis and the functional mechanism is lacking. This study intended to systematically elucidate the mechanism of T. hemsleyanum against febrile seizures.

MATERIALS AND METHODS

The efficacy of T. hemsleyanum was estimated by using a hot bath as a model of FS, the onset and duration of seizure, morphological structure changes of hippocampal neurons as well as magnetoencephalography were applied to evaluate the effects. Meanwhile, the bioactive components of T. hemsleyanum responsible for the therapeutic effect of T. hemsleyanum on FS were identified by UPLC-MS/MS. Then we systematically elucidated the mechanism of T. hemsleyanum based on metabonomics, transcriptomics, network pharmacological and experimental validation.

RESULTS

In a hyperthermia-induced FS model of rats, T. hemsleyanum significantly increased the seizure latency and decreased seizure duration, alleviating the abnormal delta and gamma band activity during epileptic discharge. Furthermore, ten chemical components of ethanol extracts from T. hemsleyanum were identified by UPLC-MS/MS, including quercetin, kaempferol, and procyanidin B1 and so on, which was consistent with the network pharmacology prediction. The serum metabolomics indicated that T. hemsleyanum mainly acts on inflammation regulation and neuroprotection by the glycerophospholipid metabolism pathway. Ninety-two potential targets of T. hemsleyanum on FS were identified by network pharmacology, and TNF, IL-6, and IL-1β were considered the pivotal targets. In the hippocampus transcriptomics, 17 KEGG pathways were identified after T. hemsleyanum treatment compared with the FS model group, among which 15 pathways overlapped with those identified by network pharmacology, and the PKC-δ/caspase-1 signaling pathway was a critical node. Finally, in vivo experiments also verified T. hemsleyanum inhibited the activation of microglia and resulted in a significant reduction in the level of PKCδ, NLRC4, caspase-1, IL-1β, IL-6 and TNF-α in hippocampus of FS rats.

CONCLUSIONS

Our study suggested that the therapeutic effect of T. hemsleyanum on FS might be regulated by inhibiting the neuroinflammation, thus exerting an anticonvulsant effect in vivo, and the mechanism might be related to regulating the PKC-δ/caspase-1 signaling pathway.

Design and synthesis of 3,4-seco-lupane triterpene derivatives to resist myocardial ischemia-reperfusion injury by inhibiting oxidative stress-mediated mitochondrial dysfunction via the PI3K/AKT/HIF-1α axis

In this study, 86 new seco-lupane triterpenoid derivatives were designed, synthesized, and characterized, and their protective activities against ischemia-reperfusion injury were investigated in vitro and in vivo. Structure-activity relationship studies revealed that most target compounds could protect cardiomyocytes against hypoxia/reoxygenation-induced injury in vitro, with compound 85 being the most active and exhibiting more potent protective activity than clinical first-line drugs. Furthermore, all thiophene derivatives exhibited stronger protective activity than furan, pyridine, and pyrazine derivatives, and the protective activity gradually increased with the extension of the alkyl chain and changed in the substituent. The data from the in-vitro and in-vivo experiments revealed that compound 85 protected mitochondria from damage by inhibiting excessive production of oxidative stressors, such as intracellular ROS, which in turn inhibited the apoptosis and necrotize of cardiomyocytes and reduced infarct size, thereby protecting normal cardiac function. It was associated with enhanced activation of the PI3K/AKT-mediated HIF-1α signaling pathway. Therefore, compound 85 acts as an oxidative stress inhibitor, blocks ROS production, protects mitochondria and cells from myocardial ischemia/reperfusion (MI/R) injury, and represents an effective new drug for treating MI/R injury.

Colon cancer transcriptome

Over the last four decades, methodological innovations have continuously changed transcriptome profiling. It is now feasible to sequence and quantify the transcriptional outputs of individual cells or thousands of samples using RNA sequencing (RNA-seq). These transcriptomes serve as a connection between cellular behaviors and their underlying molecular mechanisms, such as mutations. This relationship, in the context of cancer, provides a chance to unravel tumor complexity and heterogeneity and uncover novel biomarkers or treatment options. Since colon cancer is one of the most frequent malignancies, its prognosis and diagnosis seem to be critical. The transcriptome technology is developing for an earlier and more accurate diagnosis of cancer which can provide better protectivity and prognostic utility to medical teams and patients. A transcriptome is a whole set of expressed coding and non-coding RNAs in an individual or cell population. The cancer transcriptome includes RNA-based changes. The combined genome and transcriptome of a patient may provide a comprehensive picture of their cancer, and this information is beginning to affect treatment decision-making in real-time. A full assessment of the transcriptome of colon (colorectal) cancer has been assessed in this review paper based on risk factors such as age, obesity, gender, alcohol use, race, and also different stages of cancer, as well as non-coding RNAs like circRNAs, miRNAs, lncRNAs, and siRNAs. Similarly, they have been examined independently in the transcriptome study of colon cancer.

Machine Learning Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Cancer

Data from omics studies have been used for prediction and classification of various diseases in biomedical and bioinformatics research. In recent years, Machine Learning (ML) algorithms have been used in many different fields related to healthcare systems, especially for disease prediction and classification tasks. Integration of molecular omics data with ML algorithms has offered a great opportunity to evaluate clinical data. RNA sequence (RNA-seq) analysis has been emerged as the gold standard for transcriptomics analysis. Currently, it is being used widely in clinical research. In our present work, RNA-seq data of extracellular vesicles (EV) from healthy and colon cancer patients are analyzed. Our aim is to develop models for prediction and classification of colon cancer stages. Five different canonical ML and Deep Learning (DL) classifiers are used to predict colon cancer of an individual with processed RNA-seq data. The classes of data are formed on the basis of both colon cancer stages and cancer presence (healthy or cancer). The canonical ML classifiers, which are k-Nearest Neighbor (kNN), Logistic Model Tree (LMT), Random Tree (RT), Random Committee (RC), and Random Forest (RF), are tested with both forms of the data. In addition, to compare the performance with canonical ML models, One-Dimensional Convolutional Neural Network (1-D CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) DL models are utilized. Hyper-parameter optimizations of DL models are constructed by using genetic meta-heuristic optimization algorithm (GA). The best accuracy in cancer prediction is obtained with RC, LMT, and RF canonical ML algorithms as 97.33%. However, RT and kNN show 95.33% performance. The best accuracy in cancer stage classification is achieved with RF as 97.33%. This result is followed by LMT, RC, kNN, and RT with 96.33%, 96%, 94.66%, and 94%, respectively. According to the results of the experiments with DL algorithms, the best accuracy in cancer prediction is obtained with 1-D CNN as 97.67%. BiLSTM and LSTM show 94.33% and 93.67% performance, respectively. In classification of the cancer stages, the best accuracy is achieved with BiLSTM as 98%. 1-D CNN and LSTM show 97% and 94.33% performance, respectively. The results reveal that both canonical ML and DL models may outperform each other for different numbers of features.

Improving the Post-Operative Prediction of BCR-Free Survival Time with mRNA Variables and Machine Learning

Predicting the risk of, and time to biochemical recurrence (BCR) in prostate cancer patients post-operatively is critical in patient treatment decision pathways following surgical intervention. This study aimed to investigate the predictive potential of mRNA information to improve upon reference nomograms and clinical-only models, using a dataset of 187 patients that includes over 20,000 features. Several machine learning methodologies were implemented for the analysis of censored patient follow-up information with such high-dimensional genomic data. Our findings demonstrated the potential of inclusion of mRNA information for BCR-free survival prediction. A random survival forest pipeline was found to achieve high predictive performance with respect to discrimination, calibration, and net benefit. Two mRNA variables, namely ESM1 and DHAH8, were identified as consistently strong predictors with this dataset.

Curcumin alters distinct molecular pathways in breast cancer subtypes revealed by integrated miRNA/mRNA expression analysis

BACKGROUND

Curcumin is well known for its anticancer properties. Its cytotoxic activity has been documented in several cancer cell lines, including breast cancer. The pleiotropic activity of curcumin as an antioxidant, an antiangiogenic, antiproliferative, and pro-apoptotic, is due to its diverse targets, such as signaling pathways, protein/enzyme, or noncoding gene.

AIM

This study aimed to identify key miRNAs and mRNAs induced by curcumin in breast cancer cells MCF7, T47D (hormone positive), versus MDA-MB231 (hormone negative) using comparative analysis of global gene expression profiles.

METHODS

RNA was isolated and subjected to mRNA and miRNA library sequencing to study the global gene expression profile of curcumin-treated breast cancer cells. The differential expression of gene and miRNA was performed using the DESeq R package. The enriched pathways were studied using cluster profileR, and integrated miRNA-mRNA analysis was carried out using miRtarvis and miRmapper tools.

RESULTS

Curcumin treatment led to upregulation of 59% TSGs in MCF7, 21% in MDA-MB-231 cells, and 36% TSGs in T47D, and downregulation of 57% oncogenes in MCF7, 76% in MDA-MB-231, and 91% in T47D. Similarly, curcumin treatment led to upregulation of 32% TSmiRs in MCF7, 37.5% in MDA-MB231, and 62.5% in T47D, and downregulation of 77% oncomiRs in MCF7, 50% in MDA-MB231 and 28.6% in T47D. Integrated analysis of miRNA-mRNA led to the identification of a common NFKB pathway altered by curcumin in all three cell lines. Analysis of uniquely enriched pathway revealed non-integrin membrane-ECM interactions and laminin interactions in MCF7; extracellular matrix organization and degradation in MDA-MB-231 and cell cycle arrest and G2/M transition in T47D.

CONCLUSION

Curcumin regulates miRNA and mRNA in a cell type-specific manner. The integrative analysis led to the detection of miRNAs and mRNAs pairs, which can be used as biomarkers associated with carcinogenesis, diagnostic, and treatment response in breast cancer.

Seco-Lupane Triterpene Derivatives Induce Ferroptosis through GPX4/ACSL4 Axis and Target Cyclin D1 to Block the Cell Cycle

In this study, 70 new seco-lupane triterpene derivatives were designed, synthesized, and characterized, and their in vitro anticancer activities were evaluated. Structure-activity relationship studies showed that most compounds inhibited the growth of a variety of tumor cells in vitro. With the extension of alkyl chains, the activity of azole compounds gradually increased while that of indole compounds first increased and then decreased. Moreover, all indole derivatives showed stronger anticancer activity than azole derivatives. In addition, compound 21 showed the strongest inhibitory effect on HepG2 cells with an IC₅₀ value of 0.97 μM. Mechanistic studies showed that compound 21 coregulates the cell death process by inducing ferroptosis and regulating the cell cycle. In conclusion, compound 21 can be used as a ferroptosis inducer and cycle blocker to regulate the HepG2 death process, and it has the potential to become an effective new drug for the treatment of hepatocellular carcinoma.

Transcriptomics-Based Investigation of Molecular Mechanisms Underlying Apoptosis Induced by ZnO Nanoparticles in Human Diffuse Large B-Cell Lymphoma

Introduction

Zinc oxide nanoparticles (ZnO NPs) show anti-cancer activity. Diffuse Large B-cell Lymphoma (DLBCL) is a type of B-cell malignancies with unsatisfying treatment outcomes. This study was set to assess the potential of ZnO NPs to selectively induce apoptosis in human DLBCL cells (OCI-LY3), and to describe possible molecular mechanisms of action.

Methods

The impact of ZnO NPs on DLBCL cells and normal peripheral blood mononuclear cells (PBMCs) was studied using cytotoxicity assay and flow-cytometry. Transcriptomics analysis was conducted to identify ZnO NPs-dependent changes in the transcriptomic profiles of DLBCL cells.

Results

ZnO NPs selectively induced apoptosis in DLBCL cells, and caused changes in their transcriptomes. Deferential gene expression (DGE) with fold change (FC) ≥3 and p ≤ 0.008 with corrected p ≤ 0.05 was identified for 528 genes; 125 genes were over-expressed and 403 genes were under-expressed in ZnO NPs-treated DLBCL cells. The over-expressed genes involved in biological processes and pathways like stress response to metal ion, cellular response to zinc ion, metallothioneins bind metals, oxidative stress, and negative regulation of growth. In contrast, the under-expressed genes were implicated in DNA packaging complex, signaling by NOTCH, negative regulation of gene expression by epigenetic, signaling by WNT, M phase of cell cycle, and telomere maintenance. Setting the FC to ≥1.5 with p ≤ 0.05 and corrected p ≤ 0.1 showed ZnO NPs to induce over-expression of anti-oxidant genes and under-expression of oncogenes; target B-cell receptor (BCR) signaling pathway and NF-κB pathway; and promote apoptosis by intrinsic and extrinsic pathways.

Discussion

Overall, ZnO NPs selectively induced apoptosis in DLBCL cells, and possible molecular mechanisms of action were described.

Multi-Omics Approach Points to the Importance of Oxylipins Metabolism in Early-Stage Breast Cancer

The involvement of oxylipins, metabolites of polyunsaturated fatty acids, in cancer pathogenesis was known long ago, but only the development of the high-throughput methods get the opportunity to study oxylipins on a system level. The study aimed to elucidate alterations in oxylipin metabolism as characteristics of breast cancer patients. We compared the ultra-high-performance liquid chromatography-mass spectrometry (UPLC-MS/MS) oxylipin profile signatures in the blood plasma of 152 healthy volunteers (HC) and 169 patients with different stages of breast cancer (BC). To integrate lipidomics, transcriptomics, and genomics data, we analyzed a transcriptome of 10 open database datasets obtained from tissues and blood cells of BC patients and SNP data for 33 genes related to oxylipin metabolism. We identified 18 oxylipins, metabolites of omega-3 or omega-6 polyunsaturated fatty acids, that were differentially expressed between BCvsHC patients, including anandamide, prostaglandins and hydroxydocosahexaenoic acids. DEGs analysis of tissue and blood samples from BC patients revealed that 19 genes for oxylipin biosynthesis change their expression level, with CYP2C19, PTGS2, HPGD, and FAAH included in the list of DEGs in the analysis of transcriptomes and the list of SNPs associated with BC. Results allow us to suppose that oxylipin signatures reflect the organism's level of response to the disease. Our data regarding changes in oxylipins at the system level show that oxylipin profiles can be used to evaluate the early stages of breast cancer.

Recent Major Transcriptomics and Epitranscriptomics Contributions toward Personalized and Precision Medicine

With the advent of genome-wide screening methods-beginning with microarray technologies and moving onto next generation sequencing methods-the era of precision and personalized medicine was born. Genomics led the way, and its contributions are well recognized. However, "other-omics" fields have rapidly emerged and are becoming as important toward defining disease causes and exploring therapeutic benefits. In this review, we focus on the impacts of transcriptomics, and its extension-epitranscriptomics-on personalized and precision medicine efforts. There has been an explosion of transcriptomic studies particularly in the last decade, along with a growing number of recent epitranscriptomic studies in several disease areas. Here, we summarize and overview major efforts for cancer, cardiovascular disease, and neurodevelopmental disorders (including autism spectrum disorder and intellectual disability) for transcriptomics/epitranscriptomics in precision and personalized medicine. We show that leading advances are being made in both diagnostics, and in investigative and landscaping disease pathophysiological studies. As transcriptomics/epitranscriptomics screens become more widespread, it is certain that they will yield vital and transformative precision and personalized medicine contributions in ways that will significantly further genomics gains.

Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer

Cancer is defined as a large group of diseases that is associated with abnormal cell growth, uncontrollable cell division, and may tend to impinge on other tissues of the body by different mechanisms through metastasis. What makes cancer so important is that the cancer incidence rate is growing worldwide which can have major health, economic, and even social impacts on both patients and the governments. Thereby, the early cancer prognosis, diagnosis, and treatment can play a crucial role at the front line of combating cancer. The onset and progression of cancer can occur under the influence of complicated mechanisms and some alterations in the level of genome, proteome, transcriptome, metabolome etc. Consequently, the advent of omics science and its broad research branches (such as genomics, proteomics, transcriptomics, metabolomics, and so forth) as revolutionary biological approaches have opened new doors to the comprehensive perception of the cancer landscape. Due to the complexities of the formation and development of cancer, the study of mechanisms underlying cancer has gone beyond just one field of the omics arena. Therefore, making a connection between the resultant data from different branches of omics science and examining them in a multi-omics field can pave the way for facilitating the discovery of novel prognostic, diagnostic, and therapeutic approaches. As the volume and complexity of data from the omics studies in cancer are increasing dramatically, the use of leading-edge technologies such as machine learning can have a promising role in the assessments of cancer research resultant data. Machine learning is categorized as a subset of artificial intelligence which aims to data parsing, classification, and data pattern identification by applying statistical methods and algorithms. This acquired knowledge subsequently allows computers to learn and improve accurate predictions through experiences from data processing. In this context, the application of machine learning, as a novel computational technology offers new opportunities for achieving in-depth knowledge of cancer by analysis of resultant data from multi-omics studies. Therefore, it can be concluded that the use of artificial intelligence technologies such as machine learning can have revolutionary roles in the fight against cancer.

Research status and prospects of biomarkers for nasopharyngeal carcinoma in the era of high‑throughput omics (Review)

As a malignant tumor type, nasopharyngeal carcinoma (NPC) is characterized by distinct geographical, ethnic and genetic differences; presenting a major threat to human health in many countries, especially in Southern China. At present, no accurate and effective methods are available for the early diagnosis, efficacious evaluation or prognosis prediction for NPC. As such, a large number of patients have locoregionally advanced NPC at the time of initial diagnosis. Many patients show toxic reactions to overtreatment and have risks of cancer recurrence and distant metastasis owing to insufficient treatment. To solve these clinical problems, high‑throughput '‑omics' technologies are being used to screen and identify specific molecular biomarkers for NPC. Because of the lack of comprehensive descriptions regarding NPC biomarkers, the present study summarized the research progress that has been made in recent years to discover NPC biomarkers, highlighting the existing problems that require exploration. In view of the lack of authoritative reports at present, study design factors that affect the screening of biomarkers are also discussed here and prospects for future research are proposed to provide references for follow‑up studies of NPC biomarkers.

Cancers in Agreement? Exploring the Cross-Talk of Cancer Metabolomic and Transcriptomic Landscapes Using Publicly Available Data

One of the major hallmarks of cancer is the derailment of a cell's metabolism. The multifaceted nature of cancer and different cancer types is transduced by both its transcriptomic and metabolomic landscapes. In this study, we re-purposed the publicly available transcriptomic and metabolomics data of eight cancer types (breast, lung, gastric, renal, liver, colorectal, prostate, and multiple myeloma) to find and investigate differences and commonalities on a pathway level among different cancer types. Topological analysis of inferred graphical Gaussian association networks showed that cancer was strongly defined in genetic networks, but not in metabolic networks. Using different statistical approaches to find significant differences between cancer and control cases, we highlighted the difficulties of high-level data-merging and in using statistical association networks. Cancer transcriptomics and metabolomics and landscapes were characterized by changed macro-molecule production, however, only major metabolic deregulations with highly impacted pathways were found in liver cancer. Cell cycle was enriched in breast, liver, and colorectal cancer, while breast and lung cancer were distinguished by highly enriched oncogene signaling pathways. A strong inflammatory response was observed in lung cancer and, to some extent, renal cancer. This study highlights the necessity of combining different omics levels to obtain a better description of cancer characteristics.

Cancer Omics in Africa: Present and Prospects

During the last century, cancer biology has been arguably one of the most investigated research fields. To gain deeper insight into cancer mechanisms, scientists have been attempting to integrate multi omics data in cancer research. Cancer genomics, transcriptomics, metabolomics, proteomics, and metagenomics are the main multi omics strategies used currently in the diagnosis, prognosis, treatment, and biomarker discovery in cancer. In this review, we describe the use of different multi omics strategies in cancer research in the African continent and discuss the main challenges facing the implementation of these approaches in African countries such as the lack of training programs in bioinformatics in general and omics strategies in particular and suggest paths to address deficiencies. As a way forward, we advocate for the establishment of an "African Cancer Genomics Consortium" to promote intracontinental collaborative projects and enhance engagement in research activities that address indigenous aspects for cancer precision medicine.

Integration of Transcriptomics and Metabolomics Reveals the Antitumor Mechanism Underlying Shikonin in Colon Cancer

Colorectal cancer is a common malignancy occurring in the digestive system, which is the third common cause of cancer mortality in developed countries. Shikonin, a naphthoquinone compound extracted from the root of Lithospermum erythrorhizon, is extensively reported to exert antitumor activity against various types of cancer. However, the systematic effect of shikonin in colon cancer remains poorly understood. In the present study, we evaluated the antitumor activity of shikonin in human colon cancer cells and the therapeutic effect on a xenograft mouse model. Transcriptomics and metabolomics were further integrated to provide a systematic perspective of the shikonin-induced antitumor mechanism. The results demonstrated that shikonin had a remarkable antitumor potency both in vitro and in vivo. Moreover, metabolic pathways, including the purine metabolism, amino acid metabolism, and glycerophospholipid metabolism, were perturbed and subsequently led to cell cycle arrest in the G2/M phase. In particular, the disturbance of purine metabolism may account for the major mechanism resulting from shikonin antitumor activity.

RNA Quantification Using Noble Metal Nanoprobes: Simultaneous Identification of Several Different mRNA Targets Using Color Multiplexing and Application to Chronic Myeloid Leukemia Diagnostics

Nanotechnology provides new tools for gene expression analysis that allow for sensitive and specific characterization of prognostic signatures related to cancer. Cancer is a complex disease where multiple gene loci contribute to the phenotype. The ability to simultaneously monitor differential expression originating from each locus allows for a more accurate indication into the degree of cancerous activity than either locus alone. Metal nanoparticles have been widely used as labels for in vitro identification and quantification of target sequences.Here we describe the synthesis of nanoparticles with different noble metal compositions in an alloy format that are then functionalized with thiol-modified ssDNA (nanoprobes). We also show how such nanoprobes are used in a non-cross-linking colorimetric method for the direct detection and quantification of specific mRNA targets, without the need for enzymatic amplification or reverse-transcription steps. The different metals in the alloy provide for distinct absorption spectra due to their characteristic plasmon resonance peaks. The color multiplexing allows for simultaneous identification of different mRNA targets involved in cancer development. A comparison of the absorption spectra of the nanoprobe mixtures taken before and after induced aggregation of metal nanoparticles allows to both identify and quantify each mRNA target. We describe the use of gold and gold-silver alloy nanoprobes for the development of the non-cross-linking method to detect a specific BCR-ABL fusion gene (e.g., e1a2 and e14a2) mRNA target associated with chronic myeloid leukemia (CML) using 10 ng/μL of unamplified total human RNA. Additionally, we demonstrate the use of this approach for the direct diagnostics of CML. This simple methodology takes less than 50 min to complete after total RNA extraction with comparable specificity and sensitivity to the more commonly used methods.

Integration of transcriptomics, proteomics and metabolomics identifies biomarkers for pulmonary injury by polyhexamethylene guanidine phosphate (PHMG-p), a humidifier disinfectant, in rats

Polyhexamethylene guanidine phosphate (PHMG-p) was used as a humidifier disinfectant in Korea. PHMG induced severe pulmonary fibrosis in Koreans. The objective of this study was to elucidate mechanism of pulmonary toxicity caused by PHMG-p in rats using multi-omics analysis. Wistar rats were intratracheally instilled with PHMG-p by single (1.5 mg/kg) administration or 4-week (0.1 mg/kg, 2 times/week) repeated administration. Histopathologic examination was performed with hematoxylin and eosin staining. Alveolar macrophage aggregation and granulomatous inflammation were observed in rats treated with single dose of PHMG-p. Pulmonary fibrosis, chronic inflammation, bronchiol-alveolar fibrosis, and metaplasia of squamous cell were observed in repeated dose group. Next generation sequencing (NGS) was performed for transcriptome profiling after mRNA isolation from bronchiol-alveoli. Bronchiol-alveoli proteomic profiling was performed using an Orbitrap Q-exactive mass spectrometer. Serum and urinary metabolites were determined using 1H-NMR. Among 418 differentially expressed genes (DEGs) and 67 differentially expressed proteins (DEPs), changes of 16 mRNA levels were significantly correlated with changes of their protein levels in both single and repeated dose groups. Remarkable biological processes represented by both DEGs and DEPs were defense response, inflammatory response, response to stress, and immune response. Arginase 1 (Arg1) and lipocalin 2 (Lcn2) were identified to be major regulators for PHMG-p-induced pulmonary toxicity based on merged analysis using DEGs and DEPs. In metabolomics study, 52 metabolites (VIP > 0.5) were determined in serum and urine of single and repeated-dose groups. Glutamate and choline were selected as major metabolites. They were found to be major factors affecting inflammatory response in association with DEGs and DEPs. Arg1 and Lcn2 were suggested to be major gene and protein related to pulmonary damage by PHMG-p while serum or urinary glutamate and choline were endogenous metabolites related to pulmonary damage by PHMG-p.

Overcoming Physiological Bottlenecks of Leaf Vitality and Root Development in Cuttings: A Systemic Perspective

Each year, billions of ornamental young plants are produced worldwide from cuttings that are harvested from stock plants and planted to form adventitious roots. Depending on the plant genotype, the maturation of the cutting, and the particular environment, which is complex and often involves intermediate storage of cuttings under dark conditions and shipping between different climate regions, induced senescence or abscission of leaves and insufficient root development can impair the success of propagation and the quality of generated young plants. Recent findings on the molecular and physiological control of leaf vitality and adventitious root formation are integrated into a systemic perspective on improved physiologically-based control of cutting propagation. The homeostasis and signal transduction of the wound responsive plant hormones ethylene and jasmonic acid, of auxin, cytokinins and strigolactones, and the carbon-nitrogen source-sink balance in cuttings are considered as important processes that are both, highly responsive to environmental inputs and decisive for the development of cuttings. Important modules and bottlenecks of cutting function are identified. Critical environmental inputs at stock plant and cutting level are highlighted and physiological outputs that can be used as quality attributes to monitor the functional capacity of cuttings and as response parameters to optimize the cutting environment are discussed. Facing the great genetic diversity of ornamental crops, a physiologically targeted approach is proposed to define bottleneck-specific plant groups. Components from the field of machine learning may help to mathematically describe the complex environmental response of specific plant species.

Computational Methods to Investigate the Impact of miRNAs on Pathways

Pathway analysis is a wide class of methods allowing to determine the alteration of functional processes in complex diseases. However, biological pathways are still partial, and knowledge coming from posttranscriptional regulators has started to be considered in a systematic way only recently. Here we will give a global and updated view of the main pathway and subpathway analysis methodologies, focusing on the improvements obtained through the recent introduction of microRNAs as regulatory elements in these frameworks.

A Framework for Development of Useful Metabolomic Biomarkers and Their Effective Knowledge Translation

Despite the significant advantages of metabolomic biomarkers, no diagnostic tests based on metabolomics have been introduced to clinical use. There are many reasons for this, centered around substantial obstacles in developing clinically useful metabolomic biomarkers. Most significant is the need for interdisciplinary teams with expertise in metabolomics, analysis of complex clinical and metabolomic data, and clinical care. Importantly, the clinical need must precede biomarker discovery, and the experimental design for discovery and validation must reflect the purpose of the biomarker. Standard operating procedures for procuring and handling samples must be developed from the beginning, to ensure experimental integrity. Assay design is another challenge, as there is not much precedent informing this. Another obstacle is that it is not yet clear how to protect any intellectual property related to metabolomic biomarkers. Viewing a metabolomic biomarker as a natural phenomenon would inhibit patent protection and potentially stifle commercial interest. However, demonstrating that a metabolomic biomarker is actually a derivative of a natural phenomenon that requires innovation would enhance investment in this field. Finally, effective knowledge translation strategies must be implemented, which will require engagement with end users (clinicians and lab physicians), patient advocate groups, policy makers, and payer organizations. Addressing each of these issues comprises the framework for introducing a metabolomic biomarker to practice.

Simultaneous sequencing of coding and noncoding RNA reveals a human transcriptome dominated by a small number of highly expressed noncoding genes

Comparing the abundance of one RNA molecule to another is crucial for understanding cellular functions but most sequencing techniques can target only specific subsets of RNA. In this study, we used a new fragmented ribodepleted TGIRT sequencing method that uses a thermostable group II intron reverse transcriptase (TGIRT) to generate a portrait of the human transcriptome depicting the quantitative relationship of all classes of nonribosomal RNA longer than 60 nt. Comparison between different sequencing methods indicated that FRT is more accurate in ranking both mRNA and noncoding RNA than viral reverse transcriptase-based sequencing methods, even those that specifically target these species. Measurements of RNA abundance in different cell lines using this method correlate with biochemical estimates, confirming tRNA as the most abundant nonribosomal RNA biotype. However, the single most abundant transcript is 7SL RNA, a component of the signal recognition particle. Structured noncoding RNAs (sncRNAs) associated with the same biological process are expressed at similar levels, with the exception of RNAs with multiple functions like U1 snRNA. In general, sncRNAs forming RNPs are hundreds to thousands of times more abundant than their mRNA counterparts. Surprisingly, only 50 sncRNA genes produce half of the non-rRNA transcripts detected in two different cell lines. Together the results indicate that the human transcriptome is dominated by a small number of highly expressed sncRNAs specializing in functions related to translation and splicing.

Potential Clinical Application of Genomics in Multiple Myeloma

Multiple myeloma is a heterogeneous disease with different characteristics, and genetic aberrations play important roles in this heterogeneity. Studies have shown that these genetic aberrations are crucial in prognostication and response assessment; recent efforts have focused on their possible therapeutic implications. Despite many emerging studies being published, the best way to incorporate these results into clinical practice remains unclear. In this review paper we describe the different genomic techniques available, including the latest advancements, and discuss the potential clinical application of genomics in multiple myeloma.

Comparative analysis of gene expression in maternal peripheral blood and monocytes during spontaneous preterm labor

BACKGROUND

Preterm birth is the leading cause of newborn death worldwide, and is associated with significant cognitive and physiological challenges in later life. There is a pressing need to define the mechanisms that initiate spontaneous preterm labor, and for development of novel clinical biomarkers to identify high-risk pregnancies. Most preterm birth studies utilize fetal tissues, and there is limited understanding of the transcriptional changes that occur in mothers undergoing spontaneous preterm labor. Earlier work revealed that a specific population of maternal peripheral leukocytes (macrophages/monocytes) play an active role in the initiation of labor. Thus, we hypothesized that there are dynamic gene expression changes in maternal blood leukocytes during preterm labor.

OBJECTIVE

Using next-generation sequencing we aim to characterize the transcriptome in whole blood leukocytes and peripheral monocytes of women undergoing spontaneous preterm labor compared to healthy pregnant women who subsequently delivered at full term.

STUDY DESIGN

RNA sequencing was performed in both whole blood and peripheral monocytes from women who underwent preterm labor (24-34 weeks of gestation, N = 20) matched for gestational age to healthy pregnant controls (N = 30). All participants were a part of the Ontario Birth Study cohort (Toronto, Ontario, Canada).

RESULTS

We identified significant differences in expression of 262 genes in peripheral monocytes and 184 genes in whole blood of women who were in active spontaneous preterm labor compared to pregnant women of the same gestational age not undergoing labor, with 43 of these genes differentially expressed in both whole blood and peripheral monocytes. ADAMTS2 expression was significantly increased in women actively undergoing spontaneous preterm labor, which we validated through digital droplet reverse transcriptase polymerase chain reaction. Intriguingly, we have also identified a number of gene sets including signaling by stem cell factor-KIT, nucleotide metabolism, and trans-Golgi network vesicle budding, which exhibited changes in relative gene expression that was predictive of preterm labor status in both maternal whole blood and peripheral monocytes.

CONCLUSION

This study is the first to investigate changes in both whole blood leukocytes and peripheral monocytes of women actively undergoing spontaneous preterm labor through robust transcript measurements from RNA sequencing. Our unique study design overcame confounding based on gestational age by collecting blood samples from women matched by gestational age, allowing us to study transcriptomic changes directly related to the active preterm parturition. We performed RNA profiling using whole genome sequencing, which is highly sensitive and allowed us to identify subtle changes in specific genes. ADAMTS2 expression emerged as a marker of prematurity within peripheral blood leukocytes, an accessible tissue that plays a functional role in signaling during the onset of labor. We identified changes in relative gene expression in a number of gene sets related to signaling in monocytes and whole blood of women undergoing spontaneous preterm labor compared to controls. These genes and pathways may help identify potential targets for the development of novel drugs for preterm birth prevention.

Integrating RNA sequencing into neuro-oncology practice

Malignant tumors of the central nervous system (CNS) cause substantial morbidity and mortality, yet efforts to optimize chemo- and radiotherapy have largely failed to improve dismal prognoses. Over the past decade, RNA sequencing (RNA-seq) has emerged as a powerful tool to comprehensively characterize the transcriptome of CNS tumor cells in one high-throughput step, leading to improved understanding of CNS tumor biology and suggesting new routes for targeted therapies. RNA-seq has been instrumental in improving the diagnostic classification of brain tumors, characterizing oncogenic fusion genes, and shedding light on intratumor heterogeneity. Currently, RNA-seq is beginning to be incorporated into regular neuro-oncology practice in the form of precision neuro-oncology programs, which use information from tumor sequencing to guide implementation of personalized targeted therapies. These programs show great promise in improving patient outcomes for tumors where single agent trials have been ineffective. As RNA-seq is a relatively new technique, many further applications yielding new advances in CNS tumor research and management are expected in the coming years.

Radiogenomic Analysis of Oncological Data: A Technical Survey

In the last few years, biomedical research has been boosted by the technological development of analytical instrumentation generating a large volume of data. Such information has increased in complexity from basic (i.e., blood samples) to extensive sets encompassing many aspects of a subject phenotype, and now rapidly extending into genetic and, more recently, radiomic information. Radiogenomics integrates both aspects, investigating the relationship between imaging features and gene expression. From a methodological point of view, radiogenomics takes advantage of non-conventional data analysis techniques that reveal meaningful information for decision-support in cancer diagnosis and treatment. This survey is aimed to review the state-of-the-art techniques employed in radiomics and genomics with special focus on analysis methods based on molecular and multimodal probes. The impact of single and combined techniques will be discussed in light of their suitability in correlation and predictive studies of specific oncologic diseases.