RNA biomarkers in cancer therapeutics: The promise of personalized oncology.

Cancer therapy is a rapidly evolving and constantly expanding field. Current approaches include surgery, conventional chemotherapy and novel biologic agents as in immunotherapy, that together compose a wide armamentarium. The plethora of choices can, however, be clinically challenging in prescribing the most suitable treatment for any given patient. Fortunately, biomarkers can greatly facilitate the most appropriate selection. In recent years, RNA-based biomarkers have proven most promising. These molecules that range from small noncoding RNAs to protein coding gene transcripts can be valuable in cancer management and especially in cancer therapeutics. Compared to their DNA counterparts which are stable throughout treatment, RNA-biomarkers are dynamic. This allows prediction of success prior to treatment start and can identify alterations in expression that could reflect response. Moreover, improved nucleic acid technology allows RNA to be extracted from practically every biofluid/matrix and evaluated with exceedingly high analytic sensitivity. In addition, samples are largely obtained by minimally invasive procedures and as such can be used serially to assess treatment response real-time. This chapter provides the reader insight on currently known RNA biomarkers, the latest research employing Artificial Intelligence in the identification of such molecules and in clinical decisions driving forward the era of personalized oncology.

Mapping Single-Cell Transcriptomes of Endometrium Reveals Potential Biomarkers in Endometrial Cancer.

Background: The heterogeneity and dynamic changes of endometrial cells have a significant impact on health as they determine the normal function of the endometrium during the menstrual cycle. Dysfunction of the endometrium can lead to the occurrence of various gynecological diseases. Therefore, deconvolution of immune microenvironment that drives transcriptional programs throughout the menstrual cycle is key to understand regulatory biology of endometrium. Methods: Herein, we comprehensively analyzed single-cell transcriptome of 59,397 cells across ten human endometrium samples and revealed the dynamic cellular heterogeneity throughout the menstrual cycle. Results: We identified two perivascular cell subtypes, four epithelial subtypes and four fibroblast cell types in endometrium. Moreover, we inferred the cell type-specific transcription factor (TF) activities and linked critical TFs to transcriptional output of diverse immune cell types, highlighting the importance of transcriptional regulation in endometrium. Dynamic interactions between various types of cells in endometrium contribute to a range of biological pathways regulating differentiation of secretory. Integration of the molecular biomarkers identified in endometrium and bulk transcriptome of 535 endometrial cancers (EC), we revealed five RNA-based molecular subtypes of EC with highly intratumoral heterogeneity and different clinical manifestations. Mechanism analysis uncovered clinically relevant pathways for pathogenesis of EC. Conclusion: In summary, our results revealed the dynamic immune microenvironment of endometrium and provided novel insights into future development of RNA-based treatments for endometriosis and endometrial carcinoma.

Blood RNA Biomarkers Identify Bacterial and Biofilm Coinfections in COVID-19 Intensive Care Patients.

Purpose: Secondary opportunistic coinfections are a significant contributor to morbidity and mortality in intensive care unit (ICU) patients, but can be difficult to identify. Presently, new blood RNA biomarkers were tested in ICU patients to diagnose viral, bacterial, and biofilm coinfections. Methods: COVID-19 ICU patients had whole blood drawn in RNA preservative and stored at -80°C. Controls and subclinical infections were also studied. Droplet digital polymerase chain reaction (ddPCR) quantified 6 RNA biomarkers of host neutrophil activation to bacterial (DEFA1), biofilm (alkaline phosphatase [ALPL], IL8RB/CXCR2), and viral infections (IFI27, RSAD2). Viral titer in blood was measured by ddPCR for SARS-CoV2 (SCV2). Results: RNA biomarkers were elevated in ICU patients relative to controls. DEFA1 and ALPL RNA were significantly higher in severe versus incidental/moderate cases. SOFA score was correlated with white blood cell count (0.42), platelet count (-0.41), creatinine (0.38), and lactate dehydrogenase (0.31). ALPL RNA (0.59) showed the best correlation with SOFA score. IFI27 (0.52) and RSAD2 (0.38) were positively correlated with SCV2 viral titer. Overall, 57.8% of COVID-19 patients had a positive RNA biomarker for bacterial or biofilm infection. Conclusions: RNA biomarkers of host neutrophil activation indicate the presence of bacterial and biofilm coinfections in most COVID-19 patients. Recognizing coinfections may help to guide the treatment of ICU patients.

Integrity of RNA in long-term-stored cervical liquid-based cytology samples: implications for biomarker research.

Biobanks of cervical screening (LBC) samples annotated with disease status are an invaluable resource to support the development of tools for the risk stratification of disease. Although there is growing interest in the assessment of RNA-based biomarkers, little is known on the suitability and durability of stored clinical samples (commonly used in cervical screening) to support RNA-based research. RNA was extracted from 260 stored LBC samples. Storage at -80°C or -25°C allowed isolation of sufficient RNA for further analysis. RNA was found to be substantially degraded according to Agilent Bioanalyser data. Despite this, RT-qPCR was successful in 95% samples tested. These data suggest that biobanked LBC samples are suitable for RNA-based assessment even if stored for up to 14 years.

RNA was extracted from 260 cervical screening samples stored at either -80 or -25°C. An Agilent Bioanalyser was used to examine the level of degradation of a convenience sample of RNAs. Reverse transcriptase quantitative PCR (RT-qPCR) was used to quantify levels of two cellular mRNAs in all samples as a practical means of assessing suitability of the samples for mRNA biomarker analysis.

mRNA biomarkers sensitive and specific to micro-dose erythropoietin treatment at sea level and altitude.

Recombinant human erythropoietin (rhEPO) is prohibited by the World Anti-Doping Agency. rhEPO abuse can be indirectly detected via the athlete biological passport (ABP). However, altitude exposure challenges interpretation of the ABP. This study investigated whether 5'-aminolevulinate synthase 2 (ALAS2) and carbonic anhydrase 1 (CA1) in capillary dried blood spots (DBSs) are sensitive and specific markers of rhEPO treatment at altitude. ALAS2 and CA1 expression was monitored in DBS collected weekly before, during, and after a 3-week period at sea level or altitude. Participants were randomly assigned to receive 20 IU kg bw-1 epoetin alpha (rhEPO) or placebo injections every second day for 3 weeks while staying at sea level (rhEPO, n = 25; placebo, n = 9) or altitude (rhEPO, n = 12; placebo, n = 27). ALAS2 and CA1 expression increased up to 300% and 200%, respectively, upon rhEPO treatment at sea-level and altitude (P-values <0.05). When a blinded investigator interpreted the results, ALAS2 and CA1 expression had a sensitivity of 92%. Altitude did not confound the interpretation. Altitude affected ALAS2 and CA1 expression less than actual ABP markers when compared between sea level and altitude results. An individual athlete passport-like approach simulation confirmed the biomarker potential of ALAS2 and CA1. ALAS2 and CA1 were sensitive and specific biomarkers of micro-dose rhEPO treatment at sea level and altitude. Altitude seemed less a confounding factor for these biomarkers, especially when they are combined. Thus, micro-dose rhEPO injections can be detected in a longitudinal blinded setting using mRNA biomarkers in DBS.

Validation of potential RNA biomarkers for prostate cancer diagnosis and monitoring in plasma and urinary extracellular vesicles.

Introduction: Prostate cancer (PCa), one of the most prevalent malignancies affecting men worldwide, presents significant challenges in terms of early detection, risk stratification, and active surveillance. In recent years, liquid biopsies have emerged as a promising non-invasive approach to complement or even replace traditional tissue biopsies. Extracellular vesicles (EVs), nanosized membranous structures released by various cells into body fluids, have gained substantial attention as a source of cancer biomarkers due to their ability to encapsulate and transport a wide range of biological molecules, including RNA. In this study, we aimed to validate 15 potential RNA biomarkers, identified in a previous EV RNA sequencing study, using droplet digital PCR. Methods: The candidate biomarkers were tested in plasma and urinary EVs collected before and after radical prostatectomy from 30 PCa patients and their diagnostic potential was evaluated in a test cohort consisting of 20 benign prostate hyperplasia (BPH) and 20 PCa patients' plasma and urinary EVs. Next, the results were validated in an independent cohort of plasma EVs from 31 PCa and 31 BPH patients. Results: We found that the levels of NKX3-1 (p = 0.0008) in plasma EVs, and tRF-Phe-GAA-3b (p < 0.0001) tRF-Lys-CTT-5c (p < 0.0327), piR-28004 (p = 0.0081) and miR-375-3p (p < 0.0001) in urinary EVs significantly decreased after radical prostatectomy suggesting that the main tissue source of these RNAs is prostate and/or PCa. Two mRNA biomarkers-GLO1 and NKX3-1 showed promising diagnostic potential in distinguishing between PCa and BPH with AUC of 0.68 and 0.82, respectively, in the test cohort and AUC of 0.73 and 0.65, respectively, in the validation cohort, when tested in plasma EVs. Combining these markers in a biomarker model yielded AUC of 0.85 and 0.71 in the test and validation cohorts, respectively. Although the PSA levels in the blood could not distinguish PCa from BPH in our cohort, adding PSA to the mRNA biomarker model increased AUC from 0.71 to 0.76. Conclusion: This study identified two novel EV-enclosed RNA biomarkers-NKX3-1 and GLO1-for the detection of PCa, and highlights the complementary nature of GLO1, NKX3-1 and PSA as combined biomarkers in liquid biopsies of PCa.

Identification of tumor-agnostic biomarkers for predicting prostate cancer progression and biochemical recurrence.

The diverse clinical outcomes of prostate cancer have led to the development of gene signature assays predicting disease progression. Improved prostate cancer progression biomarkers are needed as current RNA biomarker tests have varying success for intermediate prostate cancer. Interest grows in universal gene signatures for invasive carcinoma progression. Early breast and prostate cancers share characteristics, including hormone dependence and BRCA1/2 mutations. Given the similarities in the pathobiology of breast and prostate cancer, we utilized the NanoString BC360 panel, comprising the validated PAM50 classifier and pathway-specific signatures associated with general tumor progression as well as breast cancer-specific classifiers. This retrospective cohort of primary prostate cancers (n=53) was stratified according to biochemical recurrence (BCR) status and the CAPRA-S to identify genes related to high-risk disease. Two public cohort (TCGA-PRAD and GSE54460) were used to validate the results. Expression profiling of our cohort uncovered associations between PIP and INHBA with BCR and high CAPRA-S score, as well as associations between VCAN, SFRP2, and THBS4 and BCR. Despite low levels of the ESR1 gene compared to AR, we found strong expression of the ER signaling signature, suggesting that BCR may be driven by ER-mediated pathways. Kaplan-Meier and univariate Cox proportional hazards regression analysis indicated the expression of ESR1, PGR, VCAN, and SFRP2 could predict the occurrence of relapse events. This is in keeping with the pathways represented by these genes which contribute to angiogenesis and the epithelial-mesenchymal transition. It is likely that VCAN works by activating the stroma and remodeling the tumor microenvironment. Additionally, SFRP2 overexpression has been associated with increased tumor size and reduced survival rates in breast cancer and among prostate cancer patients who experienced BCR. ESR1 influences disease progression by activating stroma, stimulating stem/progenitor prostate cancer, and inducing TGF-ß. Estrogen signaling may therefore serve as a surrogate to AR signaling during progression and in hormone-refractory disease, particularly in prostate cancer patients with stromal-rich tumors. Collectively, the use of agnostic biomarkers developed for breast cancer stratification has facilitated a precise clinical classification of patients undergoing radical prostatectomy and highlighted the therapeutic potential of targeting estrogen signaling in prostate cancer.

Machine Learning-Based Blood RNA Signature for Diagnosis of Autism Spectrum Disorder.

Early diagnosis of autism spectrum disorder (ASD) is crucial for providing appropriate treatments and parental guidance from an early age. Yet, ASD diagnosis is a lengthy process, in part due to the lack of reliable biomarkers. We recently applied RNA-sequencing of peripheral blood samples from 73 American and Israeli children with ASD and 26 neurotypically developing (NT) children to identify 10 genes with dysregulated blood expression levels in children with ASD. Machine learning (ML) analyzes data by computerized analytical model building and may be applied to building diagnostic tools based on the optimization of large datasets. Here, we present several ML-generated models, based on RNA expression datasets collected during our recently published RNA-seq study, as tentative tools for ASD diagnosis. Using the random forest classifier, two of our proposed models yield an accuracy of 82% in distinguishing children with ASD and NT children. Our proof-of-concept study requires refinement and independent validation by studies with far larger cohorts of children with ASD and NT children and should thus be perceived as starting point for building more accurate ML-based tools. Eventually, such tools may potentially provide an unbiased means to support the early diagnosis of ASD.

Prognostic Role of RNA Expression Molecular Biomarkers in Prostate and Bladder Cancers.

Prostate cancer and bladder cancer are two of the most common urologic cancers. Cancer risk stratification and prediction of prognosis have always been challenging. Following recent advances in genomic and proteomic technologies, several genomic biomarkers have been developed and proposed as a noninvasive and nonexpansive approach that can supplement our current data to improve prediction and accuracy. Several biomarkers have shown efficacy in patient risk stratification and in predicting prognosis. Here we provide a mini-review of current RNA biomarkers approved by the US Food and Drug Administration, including the Decipher, Oncotype DX Prostate, and Prolaris tests. We also provide a summary of their approved clinical utility in prostate cancer and bladder cancer management. PATIENT SUMMARY: A number of tests are available for measuring expression levels of genes related to prostate or bladder cancer. We review the usefulness of these tests in stratifying risk for patients and predicting their prognosis.

Saliva based diagnostic methodologies for a fast track detection of autism spectrum disorder: A mini-review.

Autism spectrum disorder (ASD) is considered a complicated neurodevelopment disorder with rising prevalence globally. ASD is characterized by a series of events including varying degrees of defects in communication, learning, and social interaction which is accompanied by stereotypical behavioral patterns. Despite extensive research, the current diagnosis for ASD is complex and almost solely based on the behavioral assessments of the suspected individuals. The multifactorial etiopathology of this disease along with the diversity of symptoms among different individuals adds to the current intricacies for accurate prognosis of ASD. Hence, there exists a dire need for biologically relevant biomarkers for an early diagnosis and for tracking the efficacy of therapeutic interventions. Until recently, among various biofluids, saliva has gained increasing interest for biomarker identification, the advantages include the non-invasive nature and ease of sample handling. This mini-review aims to provide a succinct summary of recent literature on saliva-based diagnostic modalities for ASD, examine various studies that highlight the potential use of proteomic and/or RNA-based biomarkers. Finally, some conclusive perspectives of using the salivary system for ASD mechanistic details and diagnosis are also discussed.

Identification of Candidate Blood mRNA Biomarkers in Intracerebral Hemorrhage Using Integrated Microarray and Weighted Gene Co-expression Network Analysis.

PURPOSE: Intracerebral hemorrhage (ICH) is a serious public health hazard due to its high morbidity, disability, and mortality. Currently, the exact molecular mechanisms of ICH are unknown. We tried to identify the ICH-related candidate blood messenger RNA (mRNA) biomarkers by microarray analysis and weighted gene co-expression network analysis (WGCNA). MATERIALS AND METHODS: We collected the blood samples from patients with ICH (n = 4) and from vascular risk factor (VRF) controls (n = 4) and analyzed the mRNA expression profiles by competitive endogenous RNA (ceRNA) microarray. Differentially expressed genes (DEGs) were identified and then a weighted gene co-expression network was constructed. Modules with clinical significance were distinguished. Then, we downloaded two Gene Expression Omnibus (GEO) datasets (GSE24265 and GSE125512). Candidate mRNAs were identified by taking the intersection of the DEGs in our microarray, the interesting genes in the key module, and the DEGs in GSE24265. Functional analysis involving Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) and construction of a protein-protein interaction (PPI) network were conducted. RESULTS: A total of 340 DEGs in our microarray were identified between the ICH group and the control group. Among the eight gene modules established by WGCNA, the yellow module containing 191 genes was the most strongly associated with ICH. Four candidate mRNAs (C3AR1, PAWR, ARNTL2, and LDLRAD4) were identified. In the early stage of ICH (within 24 h), C3AR1, PAWR, and ARNTL2 were highly expressed in the perihematomal tissue, but with low expressions in peripheral blood; in the late stage (72 h after the first blood draw), an obvious upward trend of C3AR1 and PAWR in peripheral blood was seen. Functional analysis showed that candidate mRNAs were concerned with multiple pathways, such as the Wnt signaling pathway and calcium signaling pathway. They might affect the process of ICH through neuroinflammation, cell apoptosis, and pyroptosis. CONCLUSION: We identified four candidate blood mRNAs (C3AR1, PAWR, ARNTL2, and LDLRAD4) related to ICH. They showed different expression patterns in peripheral blood and perihematomal tissues and changed with time. They might play important roles in ICH through neuroinflammation, cell apoptosis, and pyroptosis and might shed new light to novel biomarkers or therapeutic targets in ICH.

ExoProK: A Practical Method for the Isolation of Small Extracellular Vesicles from Pleural Effusions.

Extracellular vesicles (EVs) are cell-secreted, lipid membrane-enclosed nanoparticles without functional nucleus. EV is a general term that includes various subtypes of particles named microvesicles, microparticles, ectosomes or exosomes. EVs transfer RNA, DNA and protein cargo between proximal and distant cells and tissues, thus constituting an organism-wide signal transduction network. Pathological tissues secrete EVs that differ in their cargo composition compared to their healthy counterparts. The detection of biomarkers in EVs from biological fluids may aid the diagnosis of disease and/or monitor its progression in a minimally invasive manner. Among biological fluids, pleural effusions (PEs) are integrated to clinical practice, as they accompany a wide variety of lung disorders. Due to the proximity with the pleura and the lungs, PEs are expected to be especially enriched in EVs that originate from diseased tissues. However, PEs are among the least studied biofluids regarding EV-specialized isolation methods and related biomarkers. Herein, we describe a practical EV isolation method from PEs for the screening of EV RNA biomarkers in clinical routine. It is based on a Proteinase K treatment step to digest contaminants prior to standard polyethylene-glycol precipitation. The efficiency of the method was confirmed by transmission electron microscopy, nanoparticle tracking analysis and Western blot. The reliability and sensitivity of the method towards the detection of EV-enriched RNA biomarkers from multiple PEs was also demonstrated.

Housekeeping genes involved in non-malignant breast phenotypes are widely expressed in multiple cancers and provide novel biomarkers of tumor classification

Clinically relevant biomarkers are useful to determine cancer patients' prognosis and treatments. To discover new putative biomarkers, we performed in silico analysis of a 325-gene panel previously associated with breast epithelial cell biology and clinical outcomes. Sixteen public datasets of microarray samples representing 8 cancer types and a total of 3,663 patients' samples were used for the analyses. Feature selection was used to identify the best subsets of the 325 genes for each classification, and linear discriminant analysis was used to quantify the accuracy of the classifications. A subset of 102 of the 325 genes were found to be housekeeping (HK) genes, and the classifications were repeated using only the 102 HK subset. The 325-gene panel and 102 HK subset were able to distinguish colon, gastric, lung, ovarian, pancreatic, and prostate tumors and leukemia from normal adjacent tissue, and classify disease subtypes of breast and lung cancers and leukemia with 70% or higher accuracy. HK genes have been overlooked as potential biomarkers due to their relative stability. This study describes a set of HK genes as putative biomarkers applicable to multiple cancer types worth following in subsequent validation studies.

Gut Microbiota Profile Identifies Transition From Compensated Cardiac Hypertrophy to Heart Failure in Hypertensive Rats.

Microcirculatory alterations displayed by patients with heart failure (HF) induce structural and functional intestinal changes that may affect normal gut microbial community. At the same time, gut microbiota can influence pathological mechanisms implicated in HF progression. However, it is unknown whether gut microbiota dysbiosis can precede the development of cardiac alterations in HF or it is only a mere consequence. Our aim was to investigate the potential relationship between gut microbiota composition and HF development by comparing spontaneously hypertensive heart failure and spontaneously hypertensive rat models. Gut microbiota from spontaneously hypertensive heart failure, spontaneously hypertensive rat, and normotensive Wistar Kyoto rats at 9 and 19 months of age was analyzed by sequencing the 16S ribosomal RNA gene, and KEGG metabolic pathways associated to 16S profiles were predicted. Beta diversity, Firmicutes/Bacteroidetes ratio, taxonomic abundances, and potential metabolic functions of gut microbiota were significantly different in spontaneously hypertensive heart failure with respect to spontaneously hypertensive rat before (9 months) and after (19 months) cardiac differences were presented. Nine-month-old spontaneously hypertensive heart failure showed a significant increase in the genera Paraprevotella, Oscillospira, Prevotella 9, Faecalitalea, Faecalibacterium, Ruminiclostridium 6, Phascolarctobacterium, Butyrivibrio, Parasutterella, and Parabacteroides compared with both Wistar Kyoto and spontaneously hypertensive rat, while Ruminiclostridium 9, Oscillibacter, Ruminiclostridium, Mucispirillum, Intestinimonas, and Akkermansia were diminished. Of them, Akkermansia, Prevotella 9, Paraprevotella, and Phascolarctobaterium were associated to changes in cardiac structure and function. Our results demonstrate an association between specific changes in gut microbiota and the development of HF in a hypertensive model of HF and further support the intervention to restore gut microbiota as an innovative therapeutic strategy for preventing HF.

MicroRNA biomarkers for chemical hazard screening identified by RNA deep sequencing analysis in mouse embryonic stem cells.

We investigated the responses of microRNAs (miRNAs) using mouse embryonic stem cells (mESCs) exposed to nine chemicals (bis(2-ethylhexyl)phthalate, p-cresol, p-dichlorobenzene, phenol, pyrocatecol, chloroform, tri-n-butyl phosphate, trichloroethylene, and benzene), which are listed as "Class I Designated Chemical Substances" from the Japan Pollutant Release and Transfer Register. Using deep sequencing analysis (RNA-seq), several miRNAs were identified that show a substantial response to general chemical toxicity (i.e., to these nine chemicals considered as a group) and several miRNA biomarkers that show a substantial and specific response to benzene. The functions of the identified miRNAs were investigated in accordance with Gene Ontology terms of their predicted target genes, indicating regulation of cellular processes. We compared the results with those for the long non-coding RNAs (ncRNAs) and mRNAs reported in our previous studies in addition to previously identified miRNAs that are either up- or down-regulated in response to the benzene as stimuli. We also observed that the changes in expression of miRNAs were smaller than those for long ncRNAs and mRNAs. Taken together the current and previous results revealed that toxic chemical stimuli regulate the expression of miRNAs. We believe that the use of miRNAs, including the thus identified miRNAs, as biomarkers contribute to predicting the potential toxicity of particular chemicals or identifying human individuals that have been exposed to chemical hazards.

Explore prognostic marker of colorectal cancer based on ceRNA network.

Colorectal cancer (CRC) is one of the most common malignant tumors in the world. With the deepening of people's understanding of CRC at the molecular level, the survival and prognosis of CRC have been significantly improved with the help of surgery, radiotherapy, and chemotherapy, molecular targeted biological therapy and early detection of diseases. The research of different disciplines and the development of multihistological analysis in recent years have proved that the occurrence and development of CRC is a complex biological process with the common action of multiple factors, which involves the huge changes of various histological levels such as the genome, transcriptome, and epigenome. At present, the abnormal expression of protein products in the transcription process has attracted more and more attention. Based on the sensitivity and timeliness of its changes, it has become a hot topic to study the occurrence and development mechanism of CRC through transcriptome changes, so as to provide markers for early diagnosis and prognosis. In recent years, competitive endogenous RNA (ceRNA) has become one of the hot topics in cancer research. The ceRNA hypothesis holds that transcripts such as long noncoding RNA can competitively bind microRNA (miRNA), thus preventing miRNA from binding to messenger RNA (mRNA) and thereby regulating the expression of mRNA. At present, the interaction mechanism of ceRNA in CRC is still unclear, and exploring its interaction relationship is of great significance to elucidate the occurrence and development mechanism of CRC. In this study, we used The Cancer Genome Atlas (TCGA) RNA - seq data of colorectal Cancer and microRnas - seq data to construct colorectal Cancer ceRNA topology network to mine key RNAs that influence the prognosis of colorectal cancer, providing potential RNA biomarkers.

Decoding the Emerging Patterns Exhibited in Non-coding RNAs Characteristic of Lung Cancer with Regard to their Clinical Significance.

Lung cancer continues to be the leading topic concerning global mortality rate caused by can-cer; it needs to be further investigated to reduce these dramatic unfavorable statistic data. Non-coding RNAs (ncRNAs) have been shown to be important cellular regulatory factors and the alteration of their expression levels has become correlated to extensive number of pathologies. Specifically, their expres-sion profiles are correlated with development and progression of lung cancer, generating great interest for further investigation. This review focuses on the complex role of non-coding RNAs, namely miR-NAs, piwi-interacting RNAs, small nucleolar RNAs, long non-coding RNAs and circular RNAs in the process of developing novel biomarkers for diagnostic and prognostic factors that can then be utilized for personalized therapies toward this devastating disease. To support the concept of personalized medi-cine, we will focus on the roles of miRNAs in lung cancer tumorigenesis, their use as diagnostic and prognostic biomarkers and their application for patient therapy.

Amplification-Free Multi-RNA-Type Profiling for Cancer Risk Stratification via Alternating Current Electrohydrodynamic Nanomixing.

Simultaneous analysis of messenger RNA (mRNA), microRNA (miRNA), and long noncoding RNA (lncRNA)-multi-RNA-type profiling-is increasingly crucial in cancer diagnostics. Yet, rapid multi-RNA-type profiling is challenging due to enzymatic amplification reliance and RNA-type-dependent characteristics. Here, a nanodevice is reported to uniquely use alterable alternating current electrohydrodynamic (ac-EHD) forces to enhance probe-target hybridization prior to direct native RNA target detection, without target amplification or surface functionalization. To exemplify clinical applicability, noninvasive screening of next-generation prostate cancer (PCa) RNA biomarkers (of different types) in patient urine samples is performed. A strong correlation between multi-RNA-type expression and aggressive PCa is found, and the nanodevice performance is statistically evaluated. It is believed that this miniaturized system exhibits great potential for cancer risk stratification via multi-RNA-type profiling.

Glycan Stimulation Enables Purification of Prostate Cancer Circulating Tumor Cells on PEDOT NanoVelcro Chips for RNA Biomarker Detection.

A glycan-stimulated and poly(3,4-ethylene-dioxythiophene)s (PEDOT)-based nanomaterial platform is fabricated to purify circulating tumor cells (CTCs) from blood samples of prostate cancer (PCa) patients. This new platform, phenylboronic acid (PBA)-grafted PEDOT NanoVelcro, combines the 3D PEDOT nanosubstrate, which greatly enhances CTC capturing efficiency, with a poly(EDOT-PBA-co-EDOT-EG3) interfacial layer, which not only provides high specificity for CTC capture upon antibody conjugation but also enables competitive binding of sorbitol to gently release the captured cells. CTCs purified by this PEDOT NanoVelcro chip provide well-preserved RNA transcripts for the analysis of the expression level of several PCa-specific RNA biomarkers, which may provide clinical insights into the disease.