IFDlong: an isoform and fusion detector for accurate annotation and quantification of long-read RNA-seq data.

Advancements in long-read transcriptome sequencing (long-RNA-seq) technology have revolutionized the study of isoform diversity. These full-length transcripts enhance the detection of various transcriptome structural variations, including novel isoforms, alternative splicing events, and fusion transcripts. By shifting the open reading frame or altering gene expressions, studies have proved that these transcript alterations can serve as crucial biomarkers for disease diagnosis and therapeutic targets. In this project, we proposed IFDlong, a bioinformatics and biostatistics tool to detect isoform and fusion transcripts using bulk or single-cell long-RNA-seq data. Specifically, the software performed gene and isoform annotation for each long-read, defined novel isoforms, quantified isoform expression by a novel expectation-maximization algorithm, and profiled the fusion transcripts. For evaluation, IFDlong pipeline achieved overall the best performance when compared with several existing tools in large-scale simulation studies. In both isoform and fusion transcript quantification, IFDlong is able to reach more than 0.8 Spearman's correlation with the truth, and more than 0.9 cosine similarity when distinguishing multiple alternative splicing events. In novel isoform simulation, IFDlong can successfully balance the sensitivity (higher than 90%) and specificity (higher than 90%). Furthermore, IFDlong has proved its accuracy and robustness in diverse in-house and public datasets on healthy tissues, cell lines and multiple types of diseases. Besides bulk long-RNA-seq, IFDlong pipeline has proved its compatibility to single-cell long-RNA-seq data. This new software may hold promise for significant impact on long-read transcriptome analysis. The IFDlong software is available at https://github.com/wenjiaking/IFDlong.

Utility Analyses of AVITI Sequencing Chemistry.

BACKGROUND: DNA sequencing is a critical tool in modern biology. Over the last two decades, it has been revolutionized by the advent of massively parallel sequencing, leading to significant advances in the genome and transcriptome sequencing of various organisms. Nevertheless, challenges with accuracy, lack of competitive options and prohibitive costs associated with high throughput parallel short-read sequencing persist. RESULTS: Here, we conduct a comparative analysis using matched DNA and RNA short-reads assays between Element Biosciences AVITI chemistry and Illumina NextSeq 550. Similar comparisons were evaluated for synthetic long-read sequencing for RNA and targeted single-cell transcripts between the AVITI and Illumina NovaSeq 6000. For both DNA and RNA short-read applications, the study found that the AVITI produced significantly higher per sequence quality scores. For PCR-free DNA libraries, we observed up to a 10-fold lower experimentally determined error rate for using the AVITI chemistry compared to the NextSeq 550. For short-read RNA quantification, both AVITI and the NextSeq 550 demonstrated comparable accuracy. With regards to synthetic long-read mRNA and targeted synthetic long read single cell mRNA sequencing, both platforms respective chemistries performed comparably in quantification of genes and isoforms. The AVITI displayed a marginally lower error rate for long reads, with fewer chemistry-specific errors and a higher mutation detection rate. CONCLUSION: These results point to the potential of the AVITI platform as a competitive candidate in high-throughput short read sequencing analyses when juxtaposed with the Illumina NextSeq 550.

Therapeutic targeting at genome mutations of liver cancer by the insertion of HSV1 thymidine kinase through Cas9-mediated editing.

BACKGROUND: Liver cancer is one of the most lethal malignancies for humans. The treatment options for advanced-stage liver cancer remain limited. A new treatment is urgently needed to reduce the mortality of the disease. METHODS: In this report, we developed a technology for mutation site insertion of a suicide gene (herpes simplex virus type 1- thymidine kinase) based on type II CRISPR RNA-guided endonuclease Cas9-mediated genome editing to treat liver cancers. RESULTS: We applied the strategy to 3 different mutations: S45P mutation of catenin beta 1, chromosome breakpoint of solute carrier family 45 member 2-alpha-methylacyl-CoA racemase gene fusion, and V235G mutation of SAFB-like transcription modulator. The results showed that the herpes simplex virus type 1-thymidine kinase insertion rate at the S45P mutation site of catenin beta 1 reached 77.8%, while the insertion rates at the breakpoint of solute carrier family 45 member 2 - alpha-methylacyl-CoA racemase gene fusion were 95.1%-98.7%, and the insertion at V235G of SAFB-like transcription modulator was 51.4%. When these targeting reagents were applied to treat mouse spontaneous liver cancer induced by catenin beta 1S45P or solute carrier family 45 member 2-alpha-methylacyl-CoA racemase, the mice experienced reduced tumor burden and increased survival rate. Similar results were also obtained for the xenografted liver cancer model: Significant reduction of tumor volume, reduction of metastasis rate, and improved survival were found in mice treated with the targeting reagent, in comparison with the control-treated groups. CONCLUSIONS: Our studies suggested that mutation targeting may hold promise as a versatile and effective approach to treating liver cancers.

Serum Fusion Transcripts to Assess the Risk of Hepatocellular Carcinoma and the Impact of Cancer Treatment through Machine Learning.

Hepatocellular carcinoma (HCC) is one of the most fatal malignancies. Early diagnosis of HCC is crucial in reducing the risk for mortality. This study analyzed a panel of nine fusion transcripts in serum samples from 61 HCC patients and 75 patients with non-HCC conditions, using real-time quantitative RT-PCR. Seven of the nine fusions were frequently detected in HCC patients: MAN2A1-FER (100%), SLC45A2-AMACR (62.3%), ZMPSTE24-ZMYM4 (62.3%), PTEN-NOLC1 (57.4%), CCNH-C5orf30 (55.7%), STAMBPL1-FAS (26.2%), and PCMTD1-SNTG1 (16.4%). Machine-learning models were constructed based on serum fusion-gene levels to predict HCC in the training cohort, using the leave-one-out cross-validation approach. One machine-learning model, called the four fusion genes logistic regression model (MAN2A1-FER≤40, CCNH-C5orf30≤38, SLC45A2-AMACR≤41, and PTEN-NOLC1≤40), produced accuracies of 91.5% and 83.3% in the training and testing cohorts, respectively. When serum α-fetal protein level was incorporated into the machine-learning model, a two fusion gene (MAN2A1-FER≤40, CCNH-C5orf30≤38) + α-fetal protein logistic regression model was found to generate an accuracy of 94.8% in the training cohort. The same model generated 95% accuracy in both the testing and combined cohorts. Cancer treatment was associated with reduced levels of most of the serum fusion transcripts. Serum fusion-gene machine-learning models may serve as important tools in screening for HCC and in monitoring the impact of HCC treatment.

Long-read single-cell sequencing reveals expressions of hypermutation clusters of isoforms in human liver cancer cells.

The protein diversity of mammalian cells is determined by arrays of isoforms from genes. Genetic mutation is essential in species evolution and cancer development. Accurate long-read transcriptome sequencing at single-cell level is required to decipher the spectrum of protein expressions in mammalian organisms. In this report, we developed a synthetic long-read single-cell sequencing technology based on LOOPSeq technique. We applied this technology to analyze 447 transcriptomes of hepatocellular carcinoma (HCC) and benign liver from an individual. Through Uniform Manifold Approximation and Projection analysis, we identified a panel of mutation mRNA isoforms highly specific to HCC cells. The evolution pathways that led to the hyper-mutation clusters in single human leukocyte antigen molecules were identified. Novel fusion transcripts were detected. The combination of gene expressions, fusion gene transcripts, and mutation gene expressions significantly improved the classification of liver cancer cells versus benign hepatocytes. In conclusion, LOOPSeq single-cell technology may hold promise to provide a new level of precision analysis on the mammalian transcriptome.

Restoring glucose balance: Conditional HMGB1 knockdown mitigates hyperglycemia in a Streptozotocin induced mouse model.

Diabetes mellitus (DM) poses a significant global health burden, with hyperglycemia being a primary contributor to complications and high morbidity associated with this disorder. Existing glucose management strategies have shown suboptimal effectiveness, necessitating alternative approaches. In this study, we explored the role of high mobility group box 1 (HMGB1) in hyperglycemia, a protein implicated in initiating inflammation and strongly correlated with DM onset and progression. We hypothesized that HMGB1 knockdown will mitigate hyperglycemia severity and enhance glucose tolerance. To test this hypothesis, we utilized a novel inducible HMGB1 knockout (iHMGB1 KO) mouse model exhibiting systemic HMGB1 knockdown. Hyperglycemic phenotype was induced using low dose streptozotocin (STZ) injections, followed by longitudinal glucose measurements and oral glucose tolerance tests to evaluate the effect of HMGB1 knockdown on glucose metabolism. Our findings showed a substantial reduction in glucose levels and enhanced glucose tolerance in HMGB1 knockdown mice. Additionally, we performed RNA sequencing analyses, which identified potential alternations in genes and molecular pathways within the liver and skeletal muscle tissue that may account for the in vivo phenotypic changes observed in hyperglycemic mice following HMGB1 knockdown. In conclusion, our present study delivers the first direct evidence of a causal relationship between systemic HMGB1 knockdown and hyperglycemia in vivo, an association that had remained unexamined prior to this research. This discovery positions HMGB1 knockdown as a potentially efficacious therapeutic target for addressing hyperglycemia and, by extension, the DM epidemic. Furthermore, we have revealed potential underlying mechanisms, establishing the essential groundwork for subsequent in-depth mechanistic investigations focused on further elucidating and harnessing the promising therapeutic potential of HMGB1 in DM management.

Effects of the circulating environment of COVID-19 on platelet and neutrophil behavior.

Introduction: Thromboinflammatory complications are well described sequalae of Coronavirus Disease 2019 (COVID-19), and there is evidence of both hyperreactive platelet and inflammatory neutrophil biology that contributes to the thromoinflammatory milieu. It has been demonstrated in other thromboinflammatory diseases that the circulating environment may affect cellular behavior, but what role this environment exerts on platelets and neutrophils in COVID-19 remains unknown. We tested the hypotheses that 1) plasma from COVID-19 patients can induce a prothrombotic platelet functional phenotype, and 2) contents released from platelets (platelet releasate) from COVID-19 patients can induce a proinflammatory neutrophil phenotype. Methods: We treated platelets with COVID-19 patient and disease control plasma, and measured their aggregation response to collagen and adhesion in a microfluidic parallel plate flow chamber coated with collagen and thromboplastin. We exposed healthy neutrophils to platelet releasate from COVID-19 patients and disease controls and measured neutrophil extracellular trap formation and performed RNA sequencing. Results: We found that COVID-19 patient plasma promoted auto-aggregation, thereby reducing response to further stimulation ex-vivo. Neither disease condition increased the number of platelets adhered to a collagen and thromboplastin coated parallel plate flow chamber, but both markedly reduced platelet size. COVID-19 patient platelet releasate increased myeloperoxidasedeoxyribonucleic acid complexes and induced changes to neutrophil gene expression. Discussion: Together these results suggest aspects of the soluble environment circulating platelets, and that the contents released from those neutrophil behavior independent of direct cellular contact.

Long-read single-cell sequencing reveals expressions of hypermutation clusters of isoforms in human liver cancer cells.

The protein diversity of mammalian cells is determined by arrays of isoforms from genes. Genetic mutation is essential in species evolution and cancer development. Accurate Long-read transcriptome sequencing at single-cell level is required to decipher the spectrum of protein expressions in mammalian organisms. In this report, we developed a synthetic long-read single-cell sequencing technology based on LOOPseq technique. We applied this technology to analyze 447 transcriptomes of hepatocellular carcinoma (HCC) and benign liver from an individual. Through Uniform Manifold Approximation and Projection (UMAP) analysis, we identified a panel of mutation mRNA isoforms highly specific to HCC cells. The evolution pathways that led to the hyper-mutation clusters in single human leukocyte antigen (HLA) molecules were identified. Novel fusion transcripts were detected. The combination of gene expressions, fusion gene transcripts, and mutation gene expressions significantly improved the classification of liver cancer cells versus benign hepatocytes. In conclusion, LOOPseq single-cell technology may hold promise to provide a new level of precision analysis on the mammalian transcriptome.

Fusion Gene Detection in Prostate Cancer Samples Enhances the Prediction of Prostate Cancer Clinical Outcomes from Radical Prostatectomy through Machine Learning in a Multi-Institutional Analysis.

Prostate cancer remains one of the most fatal malignancies in men in the United States. Predicting the course of prostate cancer is challenging given that only a fraction of prostate cancer patients experience cancer recurrence after radical prostatectomy or radiation therapy. This study examined the expressions of 14 fusion genes in 607 prostate cancer samples from the University of Pittsburgh, Stanford University, and the University of Wisconsin-Madison. The profiling of 14 fusion genes was integrated with Gleason score of the primary prostate cancer and serum prostate-specific antigen level to develop machine-learning models to predict the recurrence of prostate cancer after radical prostatectomy. Machine-learning algorithms were developed by analysis of the data from the University of Pittsburgh cohort as a training set using the leave-one-out cross-validation method. These algorithms were then applied to the data set from the combined Stanford/Wisconsin cohort (testing set). The results showed that the addition of fusion gene profiling consistently improved the prediction accuracy rate of prostate cancer recurrence by Gleason score, serum prostate-specific antigen level, or a combination of both. These improvements occurred in both the training and testing cohorts and were corroborated by multiple models.

Oncogenic Activity of Solute Carrier Family 45 Member 2 and Alpha-Methylacyl-Coenzyme A Racemase Gene Fusion Is Mediated by Mitogen-Activated Protein Kinase.

Chromosome rearrangement is one of the hallmarks of human malignancies. Gene fusion is one of the consequences of chromosome rearrangements. In this report, we show that gene fusion between solute carrier family 45 member 2 (SLC45A2) and alpha-methylacyl-coenzyme A racemase (AMACR) occurs in eight different types of human malignancies, with frequencies ranging from 45% to 97%. The chimeric protein is translocated to the lysosomal membrane and activates the extracellular signal-regulated kinase signaling cascade. The fusion protein promotes cell growth, accelerates migration, resists serum starvation-induced cell death, and is essential for cancer growth in mouse xenograft cancer models. Introduction of SLC45A2-AMACR into the mouse liver using a sleeping beauty transposon system and somatic knockout of phosphatase and TENsin homolog (Pten) generated spontaneous liver cancers within a short period. Conclusion: The gene fusion between SLC45A2 and AMACR may be a driving event for human liver cancer development.

Transcriptome and Exome Analyses of Hepatocellular Carcinoma Reveal Patterns to Predict Cancer Recurrence in Liver Transplant Patients.

Hepatocellular carcinoma (HCC) is one of the most lethal human cancers. Liver transplantation has been an effective approach to treat liver cancer. However, significant numbers of patients with HCC experience cancer recurrence, and the selection of suitable candidates for liver transplant remains a challenge. We developed a model to predict the likelihood of HCC recurrence after liver transplantation based on transcriptome and whole-exome sequencing analyses. We used a training cohort and a subsequent testing cohort based on liver transplantation performed before or after the first half of 2012. We found that the combination of transcriptome and mutation pathway analyses using a random forest machine learning correctly predicted HCC recurrence in 86.8% of the training set. The same algorithm yielded a correct prediction of HCC recurrence of 76.9% in the testing set. When the cohorts were combined, the prediction rate reached 84.4% in the leave-one-out cross-validation analysis. When the transcriptome analysis was combined with Milan criteria using the k-top scoring pairs (k-TSP) method, the testing cohort prediction rate improved to 80.8%, whereas the training cohort and the combined cohort prediction rates were 79% and 84.4%, respectively. Application of the transcriptome/mutation pathways RF model on eight tumor nodules from 3 patients with HCC yielded 8/8 consistency, suggesting a robust prediction despite the heterogeneity of HCC. Conclusion: The genome prediction model may hold promise as an alternative in selecting patients with HCC for liver transplant.

Dynamic expression of SNAI2 in prostate cancer predicts tumor progression and drug sensitivity.

Prostate cancer is a highly heterogeneous disease, understanding the crosstalk between complex genomic and epigenomic alterations will aid in developing targeted therapeutics. We demonstrate that, even though snail family transcriptional repressor 2 (SNAI2) is frequently amplified in prostate cancer, it is epigenetically silenced in this disease, with dynamic changes in SNAI2 levels showing distinct clinical relevance. Integrative clinical data from 18 prostate cancer cohorts and experimental evidence showed that gene fusion between transmembrane serine protease 2 (TMPRSS2) and ETS transcription factor ERG (ERG) (TMPRSS2-ERG fusion) is involved in the silencing of SNAI2. We created a silencer score to evaluate epigenetic repression of SNAI2, which can be reversed by treatment with DNA methyltransferase inhibitors and histone deacetylase inhibitors. Silencing of SNAI2 facilitated tumor cell proliferation and luminal differentiation. Furthermore, SNAI2 has a major influence on the tumor microenvironment by reactivating tumor stroma and creating an immunosuppressive microenvironment in prostate cancer. Importantly, SNAI2 expression levels in part determine sensitivity to the cancer drugs dasatinib and panobinostat. For the first time, we defined the distinct clinical relevance of SNAI2 expression at different disease stages. We elucidated how epigenetic silencing of SNAI2 controls the dynamic changes of SNAI2 expression that are essential for tumor initiation and progression and discovered that restoring SNAI2 expression by treatment with panobinostat enhances dasatinib sensitivity, indicating a new therapeutic strategy for prostate cancer.

[Effects of inhibiting the expression of lncRNA PVT1 on the proliferation, apoptosis and oxidative stress of vascular endothelial cells induced by hyperglycemia].

Objective: To investigate the effects of inhibition of lncRNA PVT1 on the proliferation, apoptosis and oxidative stress of vascular endothelial cells induced by hyperglycemic. Methods: Human umbilical vein endothelial cells (HUVECs) were cultured in vitro and divided into four groups: control group (5.5 mmol/L glucose), high glucose group (30 mmol/L glucose), high glucose + siNC group (30 mmol/L glucose +siNC, negative control group), HG + siPVT1 group (30 mmol/L glucose + siPVT1, lncRNA PVT1 silencing group). The expression of PVT1 after transfection was detected by quantitative real-time PCR. MTT assay was used to detect the effect of siPVT1 (small interfering RNA PVT1) on the proliferation of HUVECs cells induced by high glucose. Flow cytometry was used to detect ROS and apoptosis of HUVECs cells induced by siPVT1. Western blot was used to detect the expression levels of apoptotic proteins such as Bax, Bcl-2, and cleaved caspase-3 in HUVECs cells. Results: Compared with the control group, after transfection with siPVT1, the expression level of PVT1 was decreased significantly (P＜0.05). MTT results showed that the proliferation activity of HUVECs cells in the high-glucose group was reduced significantly after 24 h and 48 h. Compared with the HG + siNC group, the proliferation activity of HUVECs cells in the HG + siPVT1 group was increased significantly (P＜0.05) after 24 h and 48 h. Flow cytometry results showed that ROS and apoptosis rate of HUVECs cells in the high-glucose group were increased significantly compared with the control group. Compared with the HG + siNC (negative control) group, ROS and apoptosis rates of HUVECs cells in the HG + siPVT1 group were reduced significantly. Compared with the control group, the expression levels of cleaved-caspase-3 and Bax in the high-glucose group were significantly up-regulated, while the expression level of Bcl-2 was down-regulated. Compared with the HG + siNC group, the expression levels of cleaved-caspase-3 and Bax were down-regulated, and the expression level of Bcl-2 was up-regulated. The differences were statistically significant (P＜0.05). Conclusion: Inhibition of lncRNA PVT1 can significantly increase the proliferation activity of HUVECs cells induced by hyperglycemia, reduce oxidative stress and inhibit cell apoptosis.

The Dynamic Effect of Non-CYP3A4-Metabolized and CYP3A4-Metabolized Statins on Clopidogrel Resistance in Patients With Cerebral Infarction.

Objective: To explore the treatment effect of statins used together with clopidogrel on cerebral infarction (CI). Methods: One hundred and thirty non-clopidogrel resistant patients were divided into a dynamic clopidogrel resistant (DCR) group and a continuous Non clopidogrel resistance (NCR) group. Patients were randomly assigned to AC group (atorvastatin 40 mg/d + clopidogrel, 51 patients) and RC group (rosuvastatin 20 mg/d + clopidogrel, 47 patients). The patient's platelet aggregation rate (PAR) was measured on visit 0 (baseline), visit 1 (1 week after clopidogrel alone treatment), and visits 2 to 4 (one, three, and 6 months after clopidogrel plus statins treatment). The platelet reactivity index (PRI) was assessed on visits 0, 2, and 4, and clopidogrel thiol metabolite (H4) levels was measured on visits 2 and 4. DNA sequencing was used to determine CYP3A4, CYP2C9, and CYP2C19 genotypes in all patients. Results: PAR, PRI, and H4 levels, DCR ratio, and the genotype frequencies of CYP2C9*3εC, CYP2C19*2εA, and CYP2C19*3εA of both groups were similar (p > 0.05). CYP2C19εA *2 and *3 were independent risk factors for DCR (p < 0.05). Conclusion: Clopidogrel combined with atorvastatin does not affect platelet inhibition and does not increase the incidence of DCR. The incidence of DCR in the Chinese population is high and is related to CYP2C19εA.

Detection of fusion gene transcripts in the blood samples of prostate cancer patients.

Prostate cancer remains one of the most lethal cancers for men in the United States. The study aims to detect fusion transcripts in the blood samples of prostate cancer patients. We analyzed nine fusion transcripts including MAN2A1-FER, SLC45A2-AMACR, TRMT11-GRIK2, CCNH-C5orf30, mTOR-TP53BP1, KDM4-AC011523.2, TMEM135-CCDC67, LRRC59-FLJ60017 and Pten-NOLC1147 in the blood samples from 147 prostate cancer patients and 14 healthy individuals, using Taqman RT-PCR and Sanger's sequencing. Similar analyses were also performed on 25 matched prostate cancer samples for matched-sample evaluation. Eighty-two percent blood samples from the prostate cancer patients were positive for MAN2A1-FER transcript, while 41.5% and 38.8% blood samples from the prostate cancer patients were positive for SLC45A2-AMACR and Pten-NOLC1, respectively. CCNH-c5orf30 and mTOR-TP53BP1 had low detection rates, positive in only 5.4% and 4% of the blood samples from the prostate cancer patients. Only 2 blood samples were positive for KDM4B-AC011523.2 transcript. Overall, 89.8% patients were positive for at least one fusion transcript in their blood samples. The statistical analysis showed varied sensitivity of fusion transcript detection in the blood based on the types of fusions. In contrast, the blood samples from all healthy individuals were negative for the fusion transcripts. Detection of fusion transcripts in the blood samples of the prostate cancer patients may be a fast and cost-effective way to detect prostate cancer.

TRPC3 promotes tumorigenesis of gastric cancer via the CNB2/GSK3ß/NFATc2 signaling pathway.

The transient receptor potential canonical (TRPC) channels have been implicated in various types of malignancies including gastric cancer (GC). However, the detailed mechanisms of TRPC channels underlying cell proliferation and apoptosis of GC cells remain largely unknown. Here, we report that TRPC3 was highly expressed in clinical GC specimens and correlated with GC malignant progression and poor prognosis. Forced expression of TRPC3 in GC cells enhanced both receptor-operated Ca2+ entry (ROCE) and store-operated Ca2+ entry (SOCE) and promoted the nuclear factor of activated T cell 2 (NFATc2) nuclear translocation by AKT/GSK-3ß and CNB2 signaling. Pharmacological inhibition of TRPC3 or CRISPR/Cas9-mediated TRPC3 knockout effectively inhibited the growth of GC cells both in vitro and in vivo. These effects were reversible by the rescue of TRPC3 expression. Furthermore, we confirmed the role of TRPC3 and the ROCE-AKT/GSK3ß-CNB2/NFATc2 signaling cascade in regulating cell cycle checkpoint, apoptosis cascade, and intracellular ROS production in GC. Overall, our findings suggest an oncogenic role of TRPC3 in GC and may highlight a potential target of TRPC3 for therapeutic intervention of GC and its malignant progression.

Totally Extraperitoneal Herniorrhaphy (TEP): Lessons Learned from Anatomical Observations.

BACKGROUND: Totally extraperitoneal herniorrhaphy (TEP) is a therapeutic challenge because of its complex anatomical location in inguinal region. The aim of this study was to describe the related surgical anatomy through laparoscopic observation and share the lessons learned from a review of 250 primary inguinal hernia repair procedures performed at our hospital from January 2013 to November 2019. Patients and Methods. There were 245 men and 5 women (median age: 63.2 years). Right hernia (60.2%) was the most common site. Indirect hernia (60.5%) was the most common abnormality. The classification of type II (65.0%) was the most common form. Surgical techniques comprised retromuscular approach using cauterized dissection, management of variations of arcuate line, Retzius space and Bogros space dissection, hernia sac reduction, and mesh positioning. RESULTS: The incidence of peritoneum injury was in 27 (10.1%). No epigastric vessels were injured. There were 8 (3%) hematoma and 18 (6.8%) seroma. No mesh infection, chronic pain, and recurrence were found after follow-up of an average of 35 months. CONCLUSION: A good understanding of the anatomically complex nature in the inguinal region can make it easier and safer to learn the TEP approach. Early and midterm outcomes after TEP are satisfactory.

Targeted transcriptome analysis using synthetic long read sequencing uncovers isoform reprograming in the progression of colon cancer.

The characterization of human gene expression is limited by short read lengths, high error rates and large input requirements. Here, we used a synthetic long read (SLR) sequencing approach, LoopSeq, to generate accurate sequencing reads that span full length transcripts using standard short read data. LoopSeq identified isoforms from control samples with 99.4% accuracy and a 0.01% per-base error rate, exceeding the accuracy reported for other long-read technologies. Applied to targeted transcriptome sequencing from colon cancers and their metastatic counterparts, LoopSeq revealed large scale isoform redistributions from benign colon mucosa to primary colon cancer and metastatic cancer and identified several previously unknown fusion isoforms. Strikingly, single nucleotide variants (SNVs) occurred dominantly in specific isoforms and some SNVs underwent isoform switching in cancer progression. The ability to use short reads to generate accurate long-read data as the raw unit of information holds promise as a widely accessible approach in transcriptome sequencing.

Pten-NOLC1 fusion promotes cancers involving MET and EGFR signalings.

Inactivation of Pten gene through deletions and mutations leading to excessive pro-growth signaling pathway activations frequently occurs in cancers. Here, we report a Pten derived pro-cancer growth gene fusion Pten-NOLC1 originated from a chr10 genome rearrangement and identified through a transcriptome sequencing analysis of human cancers. Pten-NOLC1 fusion is present in primary human cancer samples and cancer cell lines from different organs. The product of Pten-NOLC1 is a nuclear protein that interacts and activates promoters of EGFR, c-MET, and their signaling molecules. Pten-NOLC1 promotes cancer proliferation, growth, invasion, and metastasis, and reduces the survival of animals xenografted with Pten-NOLC1-expressing cancer cells. Genomic disruption of Pten-NOLC1 induces cancer cell death, while genomic integration of this fusion gene into the liver coupled with somatic Pten deletion produces spontaneous liver cancers in mice. Our studies indicate that Pten-NOLC1 gene fusion is a driver for human cancers.

CTCF loss mediates unique DNA hypermethylation landscapes in human cancers.

BACKGROUND: The chromatin insulator CCCTC-binding factor (CTCF) displays tissue-specific DNA binding sites that regulate transcription and chromatin organization. Despite evidence linking CTCF to the protection of epigenetic states through barrier insulation, the impact of CTCF loss on genome-wide DNA methylation sites in human cancer remains undefined. RESULTS: Here, we demonstrate that prostate and breast cancers within The Cancer Genome Atlas (TCGA) exhibit frequent copy number loss of CTCF and that this loss is associated with increased DNA methylation events that occur preferentially at CTCF binding sites. CTCF sites differ among tumor types and result in tissue-specific methylation patterns with little overlap between breast and prostate cancers. DNA methylation and transcriptome profiling in vitro establish that forced downregulation of CTCF leads to spatially distinct DNA hypermethylation surrounding CTCF binding sites, loss of CTCF binding, and decreased gene expression that is also seen in human tumors. DNA methylation inhibition reverses loss of expression at these CTCF-regulated genes. CONCLUSION: These findings establish CTCF loss as a major mediator in directing localized DNA hypermethylation events in a tissue-specific fashion and further support its role as a driver of the cancer phenotype.