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.

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.

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.

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.

A novel LncRNA-based prognostic score reveals TP53-dependent subtype of lung adenocarcinoma with poor survival.

The prognostic signatures play an essential role in the era of personalised therapy for cancer patients including lung adenocarcinoma (LUAD). Long noncoding RNA (LncRNA), a relatively novel class of RNA, has shown to play a crucial role in all the areas of cancer biology. Here, we developed and validated a robust LncRNA-based prognostic signature for LUAD patients using three different cohorts. In the discovery cohort, four LncRNAs were identified with 10% false discovery rate and a hazard ratio of >10 using univariate Cox regression analysis. A risk score, generated from the four LncRNAs' expression, was found to be a significant predictor of survival in the discovery and validation cohort (p = 9.97 × 10 -8 and 1.41 × 10 -3 , respectively). Further optimisation of four LncRNAs signature in the validation cohort, generated a three LncRNAs prognostic score (LPS), which was found to be an independent predictor of survival in both the cohorts ( p = 1.00 × 10 -6 and 7.27 × 10 -4 , respectively). The LPS also significantly divided survival in clinically important subsets, including Stage I ( p = 9.00 × 10 -4 and 4.40 × 10 -2 , respectively), KRAS wild-type (WT), KRAS mutant ( p = 4.00 × 10 -3 and 4.30 × 10 -2 , respectively) and EGFR WT ( p = 2.00 × 10 -4 ). In multivariate analysis LPS outperformed, eight previous prognosticators. Further, individual members of LPS showed a significant correlation with survival in microarray data sets. Mutation analysis showed that high-LPS patients have a higher mutation rate and inactivation of the TP53 pathway. In summary, we identified and validated a novel LncRNA signature LPS for LUAD.

Immune associated LncRNAs identify novel prognostic subtypes of renal clear cell carcinoma.

Kidney Renal Clear Cell Carcinoma (KIRC) is a significant cause of cancer-related deaths. Here, we aim to identify the LncRNAs associated with the immune system and characterise their clinical utility in KIRC. A total of 504 patients' data was used from TCGA-GDC. In silico correlation analysis identified 143 LncRNAs associated with immune-related genes (r > 0.7, P < 0.05). K-means consensus method clustered KIRC samples in three immune clusters, namely cluster C1, C2, and C3 based on the expression of 143 immune-related LncRNAs. Kaplan-Meier analysis showed that C3 patients survived significantly worse than the other two clusters (P < 0.0001). A comparison of TCGA miRNA, mRNA cluster with immune cluster showed the independence and robustness of immune clusters (HR = 2.02 and P = 2.12 × 10-8 ). The GSEA and CIBERSORT analysis showed high enrichment of poorly activated T-cells in C3 patients. To define LncRNA immune prognostic signature, we randomly divided the TCGA sample into discovery and validation sets. By utilising multivariate Cox regression analysis, we identified and validated a seven LncRNA immune prognostic signature score (LIPS score) (HR = 1.43 and P = 2.73 × 10-6 ) in KIRC. Comparison of LIPS score with all the clinical factors validated its independence and superiority in KIRC prognosis. In summary, we identified LncRNAs associated with the immune system and showed the presence of prognostic subtypes of KIRC patients based on immune-related LncRNA expression. We also identified a novel immune LncRNA based gene-signature for KIRC patients' prognostication.

Detection of 6 TFEB-amplified renal cell carcinomas and 25 renal cell carcinomas with MITF translocations: systematic morphologic analysis of 85 cases evaluated by clinical TFE3 and TFEB FISH assays.

Renal cell carcinomas with MITF aberrations demonstrate a wide morphologic spectrum, highlighting the need to consider these entities within the differential diagnosis of renal tumors encountered in clinical practice. Herein, we describe our experience with application of clinical fluorescence in situ hybridization (FISH) assays for detection of TFE3 and TFEB gene aberrations from 85 consecutive renal cell carcinoma cases submitted to our genitourinary FISH service. Results from 170 FISH assays performed on these tumors were correlated with available clinicopathologic findings. Ninety-eight percent of renal tumors submitted for FISH evaluation were from adult patients. Thirty-one (37%) tumors were confirmed to demonstrate MITF aberrations (21 TFE3 translocation, 4 TFEB translocation, and 6 TFEB amplification cases). Overall, renal cell carcinomas with MITF aberrations demonstrated morphologic features overlapping with clear cell, papillary, or clear cell papillary renal cell carcinomas. Renal cell carcinomas with MITF aberrations were significantly more likely to demonstrate dual (eosinophilic and clear) cytoplasmic tones (P=0.030), biphasic TFEB translocation renal cell carcinoma-like morphology (P=0.002), psammomatous calcifications (P=0.002), and nuclear pseudoinclusions (P=0.001) than renal cell carcinomas without MITF aberrations. Notably, 7/9 (78%) renal cell carcinomas exhibiting subnuclear clearing and linear nuclear array (6 of which showed high World Health Organization/International Society of Urological Pathology nucleolar grade) demonstrated TFE3 translocation, an association that was statistically significant when compared with renal cell carcinomas without MITF aberrations (P=0.009). In this cohort comprising consecutive cases, TFEB-amplified renal cell carcinomas were more commonly identified than renal cell carcinomas with TFEB translocations, and four (67%) of these previously unreported TFEB-amplified renal cell carcinomas demonstrated oncocytic and papillary features with a high World Health Organization/International Society of Urological Pathology nucleolar grade. In summary, TFE3 and TFEB FISH evaluation aids in identification and accurate classification of renal cell carcinomas with MITF aberrations, including TFEB-amplified renal cell carcinoma, which may demonstrate aggressive behavior.

Too sweet to resist: Control of immune cell function by O-GlcNAcylation.

O-linked ß-N-acetyl glucosamine modification (O-GlcNAcylation) is a dynamic, reversible posttranslational modification of cytoplasmic and nuclear proteins. O-GlcNAcylation depends on nutrient availability and the hexosamine biosynthetic pathway (HBP), which produces the donor substrate UDP-GlcNAc. O-GlcNAcylation is mediated by a single enzyme, O-GlcNAc transferase (OGT), which adds GlcNAc and another enzyme, O-GlcNAcase (OGA), which removes O-GlcNAc from proteins. O-GlcNAcylation controls vital cellular processes including transcription, translation, the cell cycle, metabolism, and cellular stress. Aberrant O-GlcNAcylation has been implicated in various pathologies including Alzheimer's disease, diabetes, obesity, and cancer. Growing evidences indicate that O-GlcNAcylation plays crucial roles in regulating immunity and inflammatory responses, especially under hyperglycemic conditions. This review will highlight the emerging functions of O-GlcNAcylation in mammalian immunity under physiological and various pathological conditions.

Two-pass alignment improves novel splice junction quantification.

MOTIVATION: Discovery of novel splicing from RNA sequence data remains a critical and exciting focus of transcriptomics, but reduced alignment power impedes expression quantification of novel splice junctions. RESULTS: Here, we profile performance characteristics of two-pass alignment, which separates splice junction discovery from quantification. Per sample, across a variety of transcriptome sequencing datasets, two-pass alignment improved quantification of at least 94% of simulated novel splice junctions, and provided as much as 1.7-fold deeper median read depth over those splice junctions. We further demonstrate that two-pass alignment works by increasing alignment of reads to splice junctions by short lengths, and that potential alignment errors are readily identifiable by simple classification. Taken together, two-pass alignment promises to advance quantification and discovery of novel splicing events. CONTACT: arul@med.umich.edu, nesvi@med.umich.edu AVAILABILITY AND IMPLEMENTATION: Two-pass alignment was implemented here as sequential alignment, genome indexing, and re-alignment steps with STAR. Full parameters are provided in Supplementary Table 2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Radiation-Induced Alteration of the Brain Proteome: Understanding the Role of the Sirtuin 2 Deacetylase in a Murine Model.

Whole brain radiotherapy (WBRT) produces unwanted sequelae, albeit via unknown mechanisms. A deacetylase expressed in the central nervous system, Sirtuin 2 (SIRT2), has been linked to neurodegeneration. Therefore, we sought to challenge the notion that a single disease pathway is responsible for radiation-induced brain injury in Sirt2 wild-type (WT) and knockout (KO) mice at the proteomic level. We utilized isobaric tag for relative and absolute quantitation to analyze brain homogenates from Sirt2 WT and KO mice with and without WBRT. Selected proteins were independently verified, followed by ingenuity pathway analysis. Canonical pathways for Huntington's, Parkinson's, and Alzheimer's were acutely affected by radiation within 72 h of treatment. Although loss of Sirt2 preferentially affected both Huntington's and Parkinson's pathways, WBRT most significantly affected Huntington's-related proteins in the absence of Sirt2. Identical protein expression patterns were identified in Mog following WBRT in both Sirt2 WT and KO mice, revealing a proteomic radiation signature; however, long-term radiation effects were found to be associated with altered levels of a small number of key neurodegeneration-related proteins, identified as Mapt, Mog, Snap25, and Dnm1. Together, these data demonstrate the principle that the presence of Sirt2 can have significant effects on the brain proteome and its response to ionizing radiation.

Methylation silencing of ULK2, an autophagy gene, is essential for astrocyte transformation and tumor growth.

Glioblastoma (GBM) is the most aggressive type of brain tumor and shows very poor prognosis. Here, using genome-wide methylation analysis, we show that G-CIMP+ and G-CIMP-subtypes enrich distinct classes of biological processes. One of the hypermethylated genes in GBM, ULK2, an upstream autophagy inducer, was found to be down-regulated in GBM. Promoter hypermethylation of ULK2 was confirmed by bisulfite sequencing. GBM and glioma cell lines had low levels of ULK2 transcripts, which could be reversed upon methylation inhibitor treatment. ULK2 promoter methylation and transcript levels showed significant negative correlation. Ectopic overexpression of ULK2-induced autophagy, which further enhanced upon nutrient starvation or temozolomide chemotherapy. ULK2 also inhibited the growth of glioma cells, which required autophagy induction as kinase mutant of ULK2 failed to induce autophagy and inhibit growth. Furthermore, ULK2 induced autophagy and inhibited growth in Ras-transformed immortalized Baby Mouse Kidney (iBMK) ATG5(+/+) but not in autophagy-deficient ATG5(-/-) cells. Growth inhibition due to ULK2 induced high levels of autophagy under starvation or chemotherapy utilized apoptotic cell death but not at low levels of autophagy. Growth inhibition by ULK2 also appears to involve catalase degradation and reactive oxygen species generation. ULK2 overexpression inhibited anchorage independent growth, inhibited astrocyte transformation in vitro and tumor growth in vivo. Of all autophagy genes, we found ULK2 and its homologue ULK1 were only down-regulated in all grades of glioma. Thus these results altogether suggest that inhibition of autophagy by ULK1/2 down-regulation is essential for glioma development.

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.

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.

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.

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.

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.

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.

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.

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.

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.