The nexus of long noncoding RNAs, splicing factors, alternative splicing and their modulations.

The process of alternative splicing (AS) is widely deregulated in a variety of cancers. Splicing is dependent upon splicing factors. Recently, several long noncoding RNAs (lncRNAs) have been shown to regulate AS by directly/indirectly interacting with splicing factors. This review focuses on the regulation of AS by lncRNAs through their interaction with splicing factors. AS mis-regulation caused by either mutation in splicing factors or deregulated expression of splicing factors and lncRNAs has been shown to be involved in cancer development and progression, making aberrant splicing, splicing factors and lncRNA suitable targets for cancer therapy. This review also addresses some of the current approaches used to target AS, splicing factors and lncRNAs. Finally, we discuss research challenges, some of the unanswered questions in the field and provide recommendations to advance understanding of the nexus of lncRNAs, AS and splicing factors in cancer.

Pan-cancer integrated bioinformatic analysis of RNA polymerase subunits reveal RNA Pol I member CD3EAP regulates cell growth by modulating autophagy.

Transcription is a crucial stage in gene expression. An integrated study of 34 RNA polymerase subunits (RNAPS) in the six most frequent cancer types identified several genetic and epigenetic modification. We discovered nine mutant RNAPS with a mutation frequency of more than 1% in at least one tumor type. POLR2K and POLR2H were found to be amplified and overexpressed, whereas POLR3D was deleted and downregulated. Multiple RNAPS were also observed to be regulated by variations in promoter methylation. 5-Aza-2-deoxycytidine mediated re-expression in cell lines verified methylation-driven inhibition of POLR2F and POLR2L expression in BRCA and NSCLC, respectively. Next, we showed that CD3EAP, a Pol I subunit, was overexpressed in all cancer types and was associated with worst survival in breast, liver, lung, and prostate cancers. The knockdown studies showed that CD3EAP is required for cell proliferation and induces autophagy but not apoptosis. Furthermore, autophagy inhibition rescued the cell proliferation in CD3EAP knockdown cells. CD3EAP expression correlated with S and G2 phase cell cycle regulators, and CD3EAP knockdown inhibited the expression of S and G2 CDK/cyclins. We also identified POLR2D, an RNA pol II subunit, as a commonly overexpressed and prognostic gene in multiple cancers. POLR2D knockdown also decreased cell proliferation. POLR2D is related to the transcription of just a subset of RNA POL II transcribe genes, indicating a distinct role. Taken together, we have shown the genetic and epigenetic regulation of RNAPS genes in most common tumors. We have also demonstrated the cancer-specific function of CD3EAP and POLR2D genes.

Integration of pan-cancer multi-omics data for novel mixed subgroup identification using machine learning methods.

Cancer is a heterogeneous disease, and patients with tumors from different organs can share similar epigenetic and genetic alterations. Therefore, it is crucial to identify the novel subgroups of patients with similar molecular characteristics. It is possible to propose a better treatment strategy when the heterogeneity of the patient is accounted for during subgroup identification, irrespective of the tissue of origin. This work proposes a machine learning (ML) based pipeline for subgroup identification in pan-cancer. Here, mRNA, miRNA, DNA methylation, and protein expression features from pan-cancer samples were concatenated and non-linearly projected to a lower dimension using an ML algorithm. This data was then clustered to identify multi-omics-based novel subgroups. The clinical characterization of these ML subgroups indicated significant differences in overall survival (OS) and disease-free survival (DFS) (p-value<0.0001). The subgroups formed by the patients from different tumors shared similar molecular alterations in terms of immune microenvironment, mutation profile, and enriched pathways. Further, decision-level and feature-level fused classification models were built to identify the novel subgroups for unseen samples. Additionally, the classification models were used to obtain the class labels for the validation samples, and the molecular characteristics were verified. To summarize, this work identified novel ML subgroups using multi-omics data and showed that the patients with different tumor types could be similar molecularly. We also proposed and validated the classification models for subgroup identification. The proposed classification models can be used to identify the novel multi-omics subgroups, and the molecular characteristics of each subgroup can be used to design appropriate treatment regimen.

Glutamine regulates the cellular proliferation and cell cycle progression by modulating the mTOR mediated protein levels of ß-TrCP.

The amino acid glutamine plays an important role in cell growth and proliferation. Reliance on glutamine has long been considered a hallmark of highly proliferating cancer cells. Development of strategies for cancer therapy that primarily target glutamine metabolism has been an active area of research. Glutamine depletion is associated with growth arrest and apoptosis-induced cell death; however, the molecular mechanisms involved in this process are not clearly understood. Here, we show that glutamine depletion activates the energetic stress AMPK pathway and inhibits mTORC1 activity. Furthermore, inhibition of mTORC1 reduces the protein levels of ß-TrCP, resulting in aberrant cell cycle progression and reduced proliferation. In agreement with the role of ß-TrCP in glutamine metabolism, knockdown of ß-TrCP resulted in proliferation and cell cycle defects similar to those observed for glutamine depletion. In summary, our results provide mechanistic insights into the role of glutamine metabolism in regulation of cell growth and proliferation via ß-TrCP, uncovering a previously undescribed molecular process involved in glutamine metabolism.

Computational and experimental pharmacology to decode the efficacy of Theobroma cacao L. against doxorubicin-induced organ toxicity in EAC-mediated solid tumor-induced mice.

Background and objective: Doxorubicin is extensively utilized chemotherapeutic drug, and it causes damage to the heart, liver, and kidneys through oxidative stress. Theobroma cacao L (cocoa) is reported to possess protective effects against several chemical-induced organ damages and also acts as an anticancer agent. The study aimed to determine whether the administration of cocoa bean extract reduces doxorubicin-induced organ damage in mice with Ehrlich ascites carcinoma (EAC) without compromising doxorubicin efficacy. Methodology: Multiple in vitro methods such as cell proliferation, colony formation, chemo-sensitivity, and scratch assay were carried out on cancer as well as normal cell lines to document the effect of cocoa extract (COE) on cellular physiology, followed by in vivo mouse survival analysis, and the organ-protective effect of COE on DOX-treated animals with EAC-induced solid tumors was then investigated. In silico studies were conducted on cocoa compounds with lipoxygenase and xanthine oxidase to provide possible molecular explanations for the experimental observations. Results: In vitro studies revealed potent selective cytotoxicity of COE on cancer cells compared to normal. Interestingly, COE enhanced DOX potency when used in combination. The in vivo results revealed reduction in EAC and DOX-induced toxicities in mice treated with COE, which also improved the mouse survival time; percentage of lifespan; antioxidant defense system; renal, hepatic, and cardiac function biomarkers; and also oxidative stress markers. COE reduced DOX-induced histopathological alterations. Through molecular docking and MD simulations, we observed chlorogenic acid and 8'8 methylenebiscatechin, present in cocoa, to have the highest binding affinity with lipoxygenase and xanthine oxidase, which lends support to their potential in ameliorating oxidative stress. Conclusion: The COE reduced DOX-induced organ damage in the EAC-induced tumor model and exhibited powerful anticancer and antioxidant effects. Therefore, COE might be useful as an adjuvant nutritional supplement in cancer therapy.

Machine learning based combination of multi-omics data for subgroup identification in non-small cell lung cancer.

Non-small Cell Lung Cancer (NSCLC) is a heterogeneous disease with a poor prognosis. Identifying novel subtypes in cancer can help classify patients with similar molecular and clinical phenotypes. This work proposes an end-to-end pipeline for subgroup identification in NSCLC. Here, we used a machine learning (ML) based approach to compress the multi-omics NSCLC data to a lower dimensional space. This data is subjected to consensus K-means clustering to identify the five novel clusters (C1-C5). Survival analysis of the resulting clusters revealed a significant difference in the overall survival of clusters (p-value: 0.019). Each cluster was then molecularly characterized to identify specific molecular characteristics. We found that cluster C3 showed minimal genetic aberration with a high prognosis. Next, classification models were developed using data from each omic level to predict the subgroup of unseen patients. Decision­level fused classification models were then built using these classifiers, which were used to classify unseen patients into five novel clusters. We also showed that the multi-omics-based classification model outperformed single-omic-based models, and the combination of classifiers proved to be a more accurate prediction model than the individual classifiers. In summary, we have used ML models to develop a classification method and identified five novel NSCLC clusters with different genetic and clinical characteristics.

Simple diagnosis of cancer by detecting CEA and CYFRA 21-1 in saliva using electronic sensors.

One way of early diagnosis of cancer is by detecting the biomarkers that get introduced into easily accessible body fluids. We report the development of portable and rapid electronic biosensors for quantitative detection of two secretive cancer biomarkers-Carcinoembryonic antigen (CEA) and Cytokeratin fragment 19 (CYFRA 21-1). The reduced graphene oxide (rGO)/ melamine (MEL)/antibodies/ bovine serum albumin (BSA) based devices were tested for 1 pg/mL to 800 ng/mL of CEA and CYFRA 21-1. The responses of the sensors ranged from 7.14 to 59.1% and from 6.18 to 64% for 1 pg/mL to 800 ng/mL CEA and CYFRA 21-1 respectively. A read-out circuit was assembled to develop a portable prototype which was used to assess the concentrations of the two antigens present in saliva samples of 14 subjects. The prototype could accurately discriminate between 9 oral squamous cell carcinoma patients and 5 healthy controls.

An epithelial-mesenchymal plasticity signature identifies two novel LncRNAs with the opposite regulation.

The epithelial to mesenchymal transition (EMT) is crucial for cancer progression and chemoresistance. EMT is a dynamic process with multiple phases that change cell migration and invasion activity. We used pan-cancer expression data to find 14-LncRNAs that had a high correlation with the EMT markers VIM, CDH1, FN1, SNAI1, and SNAI2. The expression of 14 EMT-associated LncRNA, which also showed high cancer specificity, was used to calculate the pan-cancer EMT score. The EMT score was then applied to the 32 cancer types to classify them as epithelial, epithelial-mesenchymal, mesenchymal-epithelial, or mesenchymal tumors. We discovered that the EMT score is a poor prognostic predictor and that as tumor mesenchymal nature increased, patient survival decreased. We also showed that the cell of origin did not influence the EMT nature of tumors. Pathway analysis employing protein expression data revealed that the PI3K pathway is the most crucial in determining the EMTness of tumors. Further, we divided CCLE-cell lines into EMT classes and discovered that mesenchymal cells, which exhibited higher PI3K pathway activation, were more sensitive to PI3K inhibitors than epithelial cells. We identified Linc01615 as a mesenchymal LncRNA whose expression significantly correlated with survival in several cancer types. We showed that Linc01615 is regulated by the TGFß-STAT3 pathway in a feedback loop. Knockdown of Linc01615 inhibited cell proliferation and migration by regulating the PI3K pathway and mesenchymal markers. We also identified RP4-568C11.4 as an epithelial cancer marker. We showed that knocking down RP4-568C11.4 decreased cell growth but not migration. In addition, we discovered that ESR1 regulates RP4-5681C11.4 in breast cancer. Taken together, we have developed a pan-cancer EMT signature. Also, we found two new LncRNAs that have different effects on cancer development and EMT.

Low temperature (Zn,Sn)O deposition for reducing interface open-circuit voltage deficit to achieve highly efficient Se-free Cu(In,Ga)S2 solar cells.

Cu(In,Ga)S2 holds the potential to become a prime candidate for use as the top cell in tandem solar cells owing to its tunable bandgap from 1.55 eV (CuInS2) to 2.50 eV (CuGaS2) and favorable electronic properties. Devices above 14% power conversion efficiency (PCE) can be achieved by replacing the CdS buffer layer with a (Zn,Mg)O or Zn(O,S) buffer layer. However, the maximum achievable PCE of these devices is limited by the necessary high heating temperatures during or after buffer deposition, as this leads to a drop in the quasi-Fermi level splitting (qFLs) and therefore the maximum achievable open-circuit voltage (VOC). In this work, a low-temperature atomic layer deposited (Zn,Sn)O thin film is explored as a buffer layer to mitigate the drop in the qFLs. The devices made with (Zn,Sn)O buffer layers are characterized by calibrated photoluminescence and current-voltage measurements to analyze the optoelectronic and electrical characteristics. An improvement in the qFLs after buffer deposition is observed for devices prepared with the (Zn,Sn)O buffer deposited at 120 °C. Consequently, a device with a VOC value above 1 V was achieved. A 14% PCE is externally measured and certified for the best solar cell. The results show the necessity of developing a low-temperature buffer deposition process to maintain and translate absorber qFLs to device VOC.

Origin of Interface Limitation in Zn(O,S)/CuInS2-Based Solar Cells.

Copper indium disulfide (CuInS2) grown under Cu-rich conditions exhibits high optical quality but suffers predominantly from charge carrier interface recombination, resulting in poor solar cell performance. An unfavorable "cliff"-like conduction band alignment at the buffer/CuInS2 interface could be a possible cause of enhanced interface recombination in the device. In this work, we exploit direct and inverse photoelectron spectroscopy together with electrical characterization to investigate the cause of interface recombination in chemical bath-deposited Zn(O,S)/co-evaporated CuInS2-based devices. Temperature-dependent current-voltage analyses indeed reveal an activation energy of the dominant charge carrier recombination path, considerably smaller than the absorber bulk band gap, confirming the dominant recombination channel to be present at the Zn(O,S)/CuInS2 interface. However, photoelectron spectroscopy measurements indicate a small (0.1 eV) "spike"-like conduction band offset at the Zn(O,S)/CuInS2 interface, excluding an unfavorable energy-level alignment to be the prominent cause for strong interface recombination. The observed band bending upon interface formation also suggests Fermi-level pinning not to be the main reason, leaving near-interface defects (as recently observed in Cu-rich CuInSe2) as the likely reason for the performance-limiting interface recombination.

Immune mechanisms in cancer patients that lead to poor outcomes of SARS-CoV-2 infection.

Patients with cancers have been severely affected by the COVID-19 pandemic. This is highlighted by the adverse outcomes in cancer patients with COVID-19 as well as by the impact of the COVID-19 pandemic on cancer care. Patients with cancer constitute a heterogeneous population that exhibits distinct mechanisms of immune dysfunction, associated with distinct systemic features of hot (T-cell-inflamed/infiltrated) and cold (Non-T-cell-inflamed and/or infiltrated) tumors. The former show hyper immune activated cells and a highly inflammatory environment while, contrastingly, the latter show the profile of a senescent and/or quiescent immune system. Thus, the evolution of SARS-CoV-2 infection in different types of cancers can show distinct trajectories which could lead to a variety of clinical and pathophysiological outcomes. The altered immunological environment including cytokines that characterizes hot and cold tumors will lead to different mechanisms of immune dysfunction, which will result in downstream effects on the course of SARS-CoV-2 infection. This review will focus on defining the known contributions of soluble pro- and anti-inflammatory mediators on immune function including altered T-cells and B-cells responses and as well on how these factors modulate the expression of SARS-CoV-2 receptor ACE2, TMPRSS2 expression, and lymph node fibrosis in cancer patients. We will propose immune mechanisms that underlie the distinct courses of SARS-CoV-2 infection in cancer patients and impact on the success of immune based therapies that have significantly improved cancer outcomes. Better understanding of the immune mechanisms prevalent in cancer patients that are associated to the outcomes of SARS-CoV-2 infection will help to identify the high-risk cancer patients and develop immune-based approaches to prevent significant adverse outcomes by targeting these pathways.

Development and validation of stemness associated LncRNA based prognostic model for lung adenocarcinoma patients.

BACKGROUND: An increasing number of studies are indicating that the stemness phenotype is a critical determinant of the Lung adenocarcinoma (LUAD) patient's response. Thus, it is crucial to identify novel biomarkers for stemness determination. OBJECTIVE: Here, we aim to develop a robust LncRNAs based prognostic signature with a stemness association for the LUAD patients. METHODS: RNA-seq and clinical data were downloaded from the existing database. The data were analysed using Cox regression, KM-plot, GSEA, and T-test. RESULTS: Initially, we used the TCGA dataset to characterize the stemness phenotype in LUAD. The commonly expressed LncRNAs in TCGA and MCTP cohort were then used as input for the Cox-regression analysis. The top three LncRNAs were selected to build a prognostic model, which was the best prognosticator in multivariate analysis with stage and previously published prognosticators. The characterization of poor surviving patients using various analysis showed high stemness properties and low expression of differentiation markers. Furthermore, we validated the prognostic score in an independent MCTP cohort of patients. In the MCTP cohort, prognostic score significantly predicted survival independent of stage and previous prognosticators. CONCLUSION: Taken together, in this study, we have developed and validated a new prognostic score associated with the stemness phenotype.

Identification and Characterization of Senescence Phenotype in Lung Adenocarcinoma with High Drug Sensitivity.

Lung adenocarcinoma (LUAD) is a major health problem and has poor prognosis. Heterogeneity is a central determinant of the treatment outcome, requiring identification of new subclasses of LUAD. Senescence has emerged as a crucial regulator of metastasis and drug response. Ionizing radiation- and doxorubicin-induced senescence-associated genes in lung fibroblasts were used in K-means clustering to identify high- and low-senescence (HS and LS) classes among The Cancer Genome Atlas- LUAD (TCGA-LUAD) patients. The LS group showed significantly poorer survival (P = 0.01) and greater activation of proliferative signaling pathways, proliferation, wound healing, and genetic aberrations (P < 0.05). The TP53 mutation rate was significantly greater in the HS group (P < 0.0001), explaining the phenotype. Also, genome-wide hypomethylation was significantly greater in the LS group than in the HS group. Interestingly, pathway analysis identified silencing of Wnt signaling in the HS group. The machine learning-based recursive feature elimination technique was used to identify a 20-gene senescence signature in TCGA-LUAD samples. The presence of a senescence phenotype with poor survival was validated in an independent patient cohort and a cell-line cohort using unsupervised clustering of samples based on a 20-gene signature. On further analysis, HS cells were more resistant to drugs, particularly histone deacetylase inhibitors. Taken together, this study identified a novel subtype of LUAD with reduced Wnt signaling and high drug resistance.

Genetic and epigenetic landscape of leukocyte infiltration identifies an immune prognosticator in lung adenocarcinoma.

BACKGROUND: Leukocyte infiltration plays an critical role in outcome of various diseases including Lung adenocarcinoma (LUAD). OBJECTIVES: To understand the genetic and epigenetic factors affecting leukocyte infiltration and identification and validation of immune based biomarkers. METHOD: Correlation analysis was done to get the associations of the factors. CIBERSORT analysis was done for immune cell infiltration. Genetic and epigenetic analysis were performed. Cox regression was carried out for survival. RESULTS: We categorized the TCGA-LUAD patients based on Leukocyte fraction (LF) and performed extensive immunogenomic analysis. Interestingly, we showed that LF has a negative correlation with copy number variation (CNV) but not with mutational load. However, several individual genetic mutations, including KRAS and KEAP1, were significantly linked with LF. Also, as expected, patients with high LF showed significantly increased expression of genes involved in leukocyte migration and activation. DNA methylation changes also showed a strong association with LF and regulated a significant proportion of genes associated with LF. We also developed and validated an independent prognostic immune signature using the top six prognostic genes associated with LF. CONCLUSION: Together, we have identified clinical, genetic, and epigenetic variations associated with LUAD LF and developed an immune gene-based signature for disease prognostication.

"Go", "No Go," or "Where to Go"; does microbiota dictate T cell exhaustion, programming, and HIV persistence?

PURPOSE OF REVIEW: People living with HIV who fail to fully reconstitute CD4+T cells after combination antiretroviral therapy therapy (i.e. immune nonresponders or INRs) have higher frequencies of exhausted T cells are enriched in a small pool of memory T cells where HIV persists and have an abundance of plasma metabolites of bacterial and host origins. Here, we review the current understanding of critical features of T cell exhaustion associated with HIV persistence; we propose to develop novel strategies to reinvigorate the effector function of exhausted T cells with the aim of purging the HIV reservoir. RECENT FINDINGS: We and others have recently reported the role of microbiota and metabolites in regulating T cell homeostasis, effector function, and senescence. We have observed that bacteria of the Firmicute phyla (specifically members of the genus Lactobacilli), associated metabolites (ß-hydroxybutyrate family), and bile acids can promote regulatory T cell differentiation in INRs with a senescent peripheral blood gene expression profile. SUMMARY: The cross-talk between immune cells and gut microbes at the intestinal mucosa (a major effector site of the mucosal immune response), regulates the priming, proliferation, and differentiation of local and distant immune responses. This cross-talk via the production of major metabolite families (like serum amyloid A, polysaccharide A, and aryl hydrocarbon receptor ligands) plays a key role in maintaining immune homeostasis. HIV infection/persistence leads to gut dysbiosis/microbial translocation, resulting in the local and systemic dissemination of microbes. The ensuing increase in immune cell-microbiome (including pathogens) interaction promotes heightened inflammatory responses and is implicated in regulating innate/adaptive immune effector differentiation cascades that drive HIV persistence. The exact role of the microbiota and associated metabolites in regulating T cell- mediated effector functions that can restrict HIV persistence continue to be the subject of on-going studies and are reviewed here.

RNA-Sequencing Analysis Pipeline for Prognostic Marker Identification in Cancer.

Sequencing analysis finds many applications in various fields of biology from comparative genomics to clinical research. Recent studies, using high-throughput sequencing method, has generated terabytes of data. It is challenging to interpret and draw a meaningful conclusion without the proper understanding of various steps involved in the analysis of such data. This chapter deals with the pipeline to be followed to process the raw RNA sequencing (RNA-Seq) reads, align, assemble, and quantify them in order to draw significant clinical conclusions from them.

Development and validation of an immune prognostic signature for ovarian carcinoma.

BACKGROUND: Ovarian cancer (OC) causes a significant proportion of cancer-related deaths in women. Recently, immunotherapy has emerged as a substantial player in cancer treatment. Lymphocyte infiltration, an important indicator of immune activity and disease aggressiveness, can be identified by gene expression profiling of immune-related genes of tumours which may prove useful in prognosis of patients. AIMS: The aim of this study is to identify and validate a novel immune gene-based prognostic signature for OC. METHODS AND RESULTS: Here, we extracted the expression of immune-related genes and performed the Cox regression analysis and identified five genes with significant correlation with survival in training cohort of patients (n = 286). We utilised regression coefficient and expression level of five genes to calculate immune prognostic signature (IPS) score for OC patients. In univariate and multivariate Cox regression analysis with other clinicopathological factors, we showed that IPS is an independent predictor of survival (P value <0.01). More importantly, we utilised 404 patients from TCGA dataset as the validation cohort and validated the survival capability of IPS in the univariate and multivariate analysis (P value <0.001). Interestingly, KM analysis showed a significant difference in survival of patients with high and low IPS score in both datasets (training dataset P value <0.01, validation dataset P value <0.01). Further, we showed that all the five genes are differentially expressed and involved in immune modulation among other pathways. Interestingly, GSEA analysis showed that high IPS patients had low immune activity and activated EMT and other oncogenic pathways. CONCLUSION: In summary, we have developed and validated robust immune-related gene-based prognostic signature to identify the OC patients with high immune activity who can be taken for immunotherapy.

A Greedy Algorithm-Based Stem Cell LncRNA Signature Identifies a Novel Subgroup of Lung Adenocarcinoma Patients With Poor Prognosis.

Cancer stem cells play an essential role in therapy response and aggressiveness of various cancers, including lung adenocarcinoma (LUAD). Interestingly it also shares many features of embryonic stem cells (ESCs). Recently, long non-coding RNAs (lncRNAs) have emerged as a critical regulator of cell physiology. Here, we used expression data of ESCs, LUAD, and normal lung to identify 198 long non-coding hESC-associated lncRNAs (hESC-lncRNAs). Intriguingly, K-means clustering of hESC-associated lncRNAs identified a subgroup of LUAD patients [undifferentiated LUAD (uLUAD)] with high stem cell-like characteristic, decreased differentiation genes expression, and poor survival. We also observed that the uLUAD patients had overexpression of proteins associated with cell proliferation. Interestingly, uLUAD patients were highly enriched with the stemness-related gene sets, and had higher mutation load. A notable result observed was high infiltration of T cells and a higher level of neopeptides in uLUAD patients, making these patients an optimal candidate for immunotherapy. Further, feature selection using greedy algorithm identified 17-hESC-lncRNAs signature, which showed significant consistency with 198 hESC-lncRNAs-based classification, and identified a group of patients with high stem cell-like characteristic in the 10 most common cancer types and CCLE cell lines. These results suggest the conventional role of hESC-lncRNAs in stem cell biology. In summary, we identified a novel subgroup of LUAD patients (uLUAD) using a set of hESC-lncRNAs. The uLUAD patients had high stem cell-like characteristic and reduced survival rate and may be referred for immunotherapy. Furthermore, our analysis also showed the importance of lncRNAs in cancer and cancer stem cells.

Sirt2 Regulates Radiation-Induced Injury.

Sirtuin 2 (SIRT2) plays a major role in aging, carcinogenesis and neurodegeneration. While it has been shown that SIRT2 is a mediator of stress-induced cell death, the mechanism remains unclear. In this study, we report the role of SIRT2 in mediating radiation-induced cell death and DNA damage using mouse embryonic fibroblasts (MEFs), progenitor cells and tissues from Sirt2 wild-type and genomic knockout mice, and human tumor and primary cell lines as models. The presence of Sirt2 in cells and tissues significantly enhanced the cell's sensitivity to radiation-induced cytotoxicity by delaying the dispersion of radiation-induced γ-H2AX and 53BP1 foci. This enhanced cellular radiosensitivity correlated with reduced expression of pro-survival and DNA repair proteins, and decreased DNA repair capacities involving both homologous repair and non-homologous end joining DNA repair mechanisms compared to those in Sirt2 knockout (KO) and knockdown (KD) phenotypes. Together, these data suggest SIRT2 plays a critical role in mediating the radiation-induced DNA damage response, thus regulating radiation-induced cell death and survival.

The Landscape of Circular RNA in Cancer.

Circular RNAs (circRNAs) are an intriguing class of RNA due to their covalently closed structure, high stability, and implicated roles in gene regulation. Here, we used an exome capture RNA sequencing protocol to detect and characterize circRNAs across >2,000 cancer samples. When compared against Ribo-Zero and RNase R, capture sequencing significantly enhanced the enrichment of circRNAs and preserved accurate circular-to-linear ratios. Using capture sequencing, we built the most comprehensive catalog of circRNA species to date: MiOncoCirc, the first database to be composed primarily of circRNAs directly detected in tumor tissues. Using MiOncoCirc, we identified candidate circRNAs to serve as biomarkers for prostate cancer and were able to detect circRNAs in urine. We further detected a novel class of circular transcripts, termed read-through circRNAs, that involved exons originating from different genes. MiOncoCirc will serve as a valuable resource for the development of circRNAs as diagnostic or therapeutic targets across cancer types.