Comparative modeling of the mitral valve in normal and prolapse conditions.

Introduction: Computational modeling is one of the best non-invasive approaches to predicting the functional behavior of the mitral valve (MV) in health and disease. Mitral valve prolapse (MVP) due to partial or complete chordae tendineae rapture is the most common valvular disease and results in mitral regurgitation (MR). Methods: In this study, Image-based fluid-structure interaction (FSI) models of the human MV are developed in the normal physiological and posterior leaflet prolapse conditions. Detailed geometry of the healthy human MV is derived from Computed Tomography imaging data. To provide prolapse condition, some chords attached to the posterior leaflet are removed from the healthy valve. Both normal and prolapsed valves are embedded separately in a straight tubular blood volume and simulated under physiological systolic pressure loads. The Arbitrary Lagrangian-Eulerian finite element method is used to accommodate the deforming intersection boundaries of the blood and MV. Results: The stress values in the mitral components, and also flow patterns including the regurgitant flow rates are obtained and compared in both conditions through the simulation. These simulations have the potential to improve the treatment of patients with MVP, and also help surgeons to have more realistic insight into the dynamics of the MV in health and prolapse. Conclusion: In the prolapse model, computational results show incomplete leaflet coaptation, higher MR severity, and also a significant increment of posterior leaflet stress compared to the normal valve. Moreover, it is found more deviation of the regurgitant jet towards the left atrium wall due to the posterior leaflet prolapse.

Introducing radiomics model to predict active plaque in multiple sclerosis patients using magnetic resonance images.

Multiple Sclerosis (MS) is the most common non-traumatic disabling disease in young people. The prediction active plaque has the potential to offer new biomarkers for assessing the activity of MS disease. Consequently it supports patient management in the clinical setting and trials. This study aims to investigate the predictive capability of radiomics features for identifying active plaques in these patients using T2 FLAIR (Fluid Attenuated Inversion Recovery) images. For this purpose, a dataset images from 82 patients with 122 lesions was analyzed. Feature selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO) method. Six different classifier algorithms, namely K-Nearest Neighbors (KNN), Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF), were employed for modeling. The models were evaluated using 5-fold cross-validation, and performance metrics including sensitivity, specificity, accuracy, area under the curve (AUC), and mean squared error were computed. A total of 107 radiomics features were extracted for each lesion, and 11 robust features were identified through the feature selection process. These features consisted of four shape features (elongation, flatness, major axis length, mesh volume), one first-order feature (energy), one Gray Level Co-occurrence Matrix feature (correlation), two Gray Level Run Length Matrix features (gray level non-uniformity, gray level non-uniformity normalized), and three Gray Level Size Zone Matrix features (low gray level zone emphasis, size zone non-uniformity, small area low gray level emphasis). The NB classifier demonstrated the best performance with an AUC, sensitivity, and specificity of 0.85, 0.82, and 0.66, respectively. The findings indicate the potential of radiomics features in predicting active MS plaques in T2 FLAIR images.

Oncogenic fusion transcript analysis identified ADAP1-NOC4L, potentially associated with metastatic colorectal cancer.

PURPOSE: Fusion transcripts are transcriptome-mediated alterations involved in tumorigenesis and are considered as diagnostic, prognostic, and therapeutic biomarkers. In metastatic colorectal carcinoma (mCRC), fusion transcripts are rarely reported. The main challenge is to identify driver chimeras with a significant role in cancer progression. METHODS: In the present study, 86 RNA sequencing data samples were analyzed to discover driver fusion transcripts. Functional assays included clonogenic cell survival, wound-healing, and transwell cell invasion. Quantitative expression analysis of epithelial-mesenchymal transition (EMT), apoptotic regulators, and metastatic markers were examined for the candidate fusion genes. Kaplan-Meier survival analysis was performed using patient overall survival (OS). RESULTS: A variety of driver fusions were identified. Fourteen fusion genes (51% of mCRC), each at least found in two mCRC samples, were determined as oncogenic fusion transcripts by in silico analysis of their functions. Among them, two recurrent chimeric transcripts confirmed by Sanger sequencing were selected. Positive expression of ADAP1-NOC4L was significantly associated with an increased risk of poor OS in mCRC patients. In vitro transforming potential for the chimera, resulting from the fusion of ADAP1 and NOC4L was assessed. Overexpression of this fusion gene increased cell proliferation and enhanced migration and invasion of CRC cells. In addition, it significantly upregulated EMT and anti-apoptotic markers. CONCLUSIONS: ADAP1-NOC4L transcript chimera, a driver chimera identified in this study, provides new insight into the underlying mechanisms involved in the development and spread of mCRC. It suggests the potential of RNA-based alterations as novel targets for personalized medicine in clinical practice.

Integrated genomic sequencing in myeloid blast crisis chronic myeloid leukemia (MBC-CML), identified potentially important findings in the context of leukemogenesis model.

Chronic myeloid leukemia (CML) is a model of leukemogenesis in which the exact molecular mechanisms underlying blast crisis still remained unexplored. The current study identified multiple common and rare important findings in myeloid blast crisis CML (MBC-CML) using integrated genomic sequencing, covering all classes of genes implicated in the leukemogenesis model. Integrated genomic sequencing via Whole Exome Sequencing (WES), Chromosome-seq and RNA-sequencing were conducted on the peripheral blood samples of three CML patients in the myeloid blast crisis. An in-house filtering pipeline was applied to assess important variants in cancer-related genes. Standard variant interpretation guidelines were used for the interpretation of potentially important findings (PIFs) and potentially actionable findings (PAFs). Single nucleotide variation (SNV) and small InDel analysis by WES detected sixteen PIFs affecting all five known classes of leukemogenic genes in myeloid malignancies including signaling pathway components (ABL1, PIK3CB, PTPN11), transcription factors (GATA2, PHF6, IKZF1, WT1), epigenetic regulators (ASXL1), tumor suppressor and DNA repair genes (BRCA2, ATM, CHEK2) and components of spliceosome (PRPF8). These variants affect genes involved in leukemia stem cell proliferation, self-renewal, and differentiation. Both patients No.1 and No.2 had actionable known missense variants on ABL1 (p.Y272H, p.F359V) and frameshift variants on ASXL1 (p.A627Gfs*8, p.G646Wfs*12). The GATA2-L359S in patient No.1, PTPN11-G503V and IKZF1-R208Q variants in the patient No.3 were also PAFs. RNA-sequencing was used to confirm all of the identified variants. In the patient No. 3, chromosome sequencing revealed multiple pathogenic deletions in the short and long arms of chromosome 7, affecting at least three critical leukemogenic genes (IKZF1, EZH2, and CUX1). The large deletion discovered on the short arm of chromosome 17 in patient No. 2 resulted in the deletion of TP53 gene as well. Integrated genomic sequencing combined with RNA-sequencing can successfully discover and confirm a wide range of variants, from SNVs to CNVs. This strategy may be an effective method for identifying actionable findings and understanding the pathophysiological mechanisms underlying MBC-CML, as well as providing further insights into the genetic basis of MBC-CML and its management in the future.

Transcriptome analysis of colorectal cancer liver metastasis: The importance of long non-coding RNAs and fusion transcripts in the disease pathogenesis.

BACKGROUND: Despite several attempts to define the many genomic aspects of colorectal cancer liver metastasis (CRC-LM), there is still a lack of a complete and accurate picture of the cancer transcriptome and its function in the generation of metastasis. METHODS: Cancer Genome Atlas Sequence Read Archive (SRA) was used to get RNA sequencing data for CRC-LM and primary CRC. The CDseqR deconvolution method followed by the edgeR statistical approach was employed to accurately find differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) was used to determine the long non-coding RNA (lncRNA) and mRNA pairs in CRC-LM etiology. Three alternative methods were used to explore fusion transcripts to anticipate the potential driver chimeras. RESULTS: Multiple cancer-related pathways were enriched in the up-regulated genes, including cell cycle, DNA replication, and RNA transport. SPP1 was the most up-regulated gene important in the cellular proliferation and migration and CCDC152 was the most down-regulated gene known in the metastatic spread of CRC. There were seven distinct lncRNAs discovered, two of which were novel (LOC107984834 and LOC107985040) and associated with metastatic related pathways such as the extracellular matrix-receptor interaction. Overall survival analysis demonstrated that SPP1 and LOC107985040 were significantly associated with poor prognosis outcomes. Seven new fusion transcripts were found in seven CRC-LM patients (22.5%) anticipated to have potential driver functions in cancer. CONCLUSION: The newly discovered dysregulated genes and other transcriptome abnormalities could contribute to a better understanding of the CRC-LM underlying mechanism, leading to the development of new diagnostic, prognostic, and therapeutic molecular options for personalized medicine.

Fusion transcript discovery using RNA sequencing in formalin-fixed paraffin-embedded specimen.

Chimeric transcripts are critical for diagnosis or prognosis and could constitute effective therapeutic targets. Fresh tissues are the major source for the identification of these fusion transcripts. The quality and quantity of the extracted RNA directly affect fusion transcript discovery. Formalin-fixed paraffin-embedded (FFPE) tissues allow long-time preservation of tumor histology for microscopic evaluation; however, no provision has been made for either the type of fixative or embedding procedure used for preserving RNA. Nonetheless, the widespread use of these FFPE tissues in translational and clinical research prompts to overcome these issues. RNA is, by nature, of reduced quality and amount in these FFPE tissues. Therefore, attempts should be taken to minimize the limitations of FFPE tissues as a widely available source of fusion transcript identification. In this review, we describe approaches allowing fusion transcript identification from FFPE tissues using RNA sequencing techniques.

Crosstalk between DNA methylation and gene expression in colorectal cancer, a potential plasma biomarker for tracing this tumor.

Colorectal cancer (CRC), the second leading cause of cancer mortality, constitutes a significant global health burden. An accurate, noninvasive detection method for CRC as complement to colonoscopy could improve the effectiveness of treatment. In the present study, SureSelectXT Methyl-Seq was performed on cancerous and normal colon tissues and CLDN1, INHBA and SLC30A10 were found as candidate methylated genes. MethyLight assay was run on formalin-fixed paraffin-embedded (FFPE) and fresh case and control tissues to validate the methylation of the selected gene. The methylation was significantly different (p-values < 2.2e-16) with a sensitivity of 87.17%; at a specificity cut-off of 100% in FFPE tissues. Methylation studies on fresh tissues, indicated a sensitivity of 82.14% and a specificity cut-off of 92% (p-values = 1.163e-07). The biomarker performance was robust since, normal tissues indicated a significant 22.1-fold over-expression of the selected gene as compared to the corresponding CRC tissues (p-value < 2.2e-16) in the FFPE expression assay. In our plasma pilot study, evaluation of the tissue methylation marker in the circulating cell-free DNA, demonstrated that 9 out of 22 CRC samples and 20 out of 20 normal samples were identified correctly. In summary, there is a clinical feasibility that the offered methylated gene could serve as a candidate biomarker for CRC diagnostic purpose, although further exploration of our candidate gene is warranted.

Distribution of myofibroblast cells and microvessels around invasive ductal carcinoma of the breast and comparing with the adjacent range of their normal-to-DCIS zones.

BACKGROUND: This study seeks to determine the relationships between manifestation of myofibroblasts in the stroma tissue of hyperplastic pre-invasive breast lesions to invasive cancer by investigating clinicopathological data of patients, their effect on steroid receptor expression and HER2, and angiogenesis according to CD34 antigen expression. METHODS: 100 cases of invasive ductal carcinoma were immunohistochemically investigated for the presence of smooth muscle actin (SMA), ER/PR, HER2, anti-CD34 antibody and microvessel count (MVC). Patients were scored in four different zones of invasive areas: invasive cancer, DCIS, fibrocystic disease ± ductal intraepithelial neoplasia (FCD ± DIN), and normal tissue.  RESULTS: There was a significant difference in stromal myofibroblasts between all areas except for the stroma of DCIS and FCD ± DIN (P < 0.001). We observed positive significant correlations between stromal myofibroblasts, HER2 expression, and the numbers of involved lymph nodes in invasive cancer, DCIS, and FCD ± DIN (P < 0.001). More myofibroblasts were present in grade III cases, with the least frequent observed among grade I cases in the stroma of those with invasive disease, DCIS, and FCD ± DIN (P < 0.001).  MVC was inversely related to stromal myofibroblasts in invasive cancer (P < 0.001) and DCIS (P < 0.001), whereas there was a positive correlation in the stroma of FCD ± DIN (P = 0.002) and normal areas (P = 0.054). There was a significant difference in MVC observed in all areas except for DCIS and FCD ± DIN (P < 0.001). We noted significant inverse correlations between MVC, HER2 expression, and the numbers of involved lymph nodes in invasive cancer and DCIS (P < 0.001). Most MVC were present in grade I, with the least frequent observed in grade III cases in the stroma of invasive cancer, DCIS and FCD ± DIN (P < 0.001).  CONCLUSION: Angiogenesis can be observed before any significant myofibroblastic changes in the pre-invasive breast lesions. The elevated content of myofibroblasts in stroma of tumor; probably may be a worse prognostic factor  and the steps from atypical epithelial hyperplasia to DCIS and then to the invasive carcinoma do not appear to be always part of a linear progression.