Since it was first described, the imprinted cluster 11p15.5 has been reported to be deregulated in a variety of pediatric and adult cancers including that of the lung. Both protein coding and ...non-coding genes functioning as oncogenes or as tumor suppressor genes reside within this cluster. Oncomirs that can function as oncogenes or as tumor suppressors have also been reported. While a complete account of the role played by the 11p15.5 imprinted cluster in lung cancer is beyond the scope of this review, we will focus on the role of the non-coding RNAs processed from the H19-IGF2 loci. A special emphasis will be given to the H19/miR-675 gene locus. Their potential diagnostic and therapeutic use in lung cancer will be described.
Early detection of colorectal cancer (CRC) is currently based on fecal occult blood testing (FOBT) and colonoscopy, both which can significantly reduce CRC‐related mortality. However, FOBT has ...low‐sensitivity and specificity, whereas colonoscopy is labor‐ and cost‐intensive. Therefore, the discovery of novel biomarkers that can be used for improved CRC screening, diagnosis, staging and as targets for novel therapies is of utmost importance. To identify novel CRC biomarkers we utilized representational difference analysis (RDA) and characterized a colon cancer associated transcript (CCAT1), demonstrating consistently strong expression in adenocarcinoma of the colon, while being largely undetectable in normal human tissues (p < 000.1). CCAT1 levels in CRC are on average 235‐fold higher than those found in normal mucosa. Importantly, CCAT1 is strongly expressed in tissues representing the early phase of tumorigenesis: in adenomatous polyps and in tumor‐proximal colonic epithelium, as well as in later stages of the disease (liver metastasis, for example). In CRC‐associated lymph nodes, CCAT1 overexpression is detectable in all H&E positive, and 40.0% of H&E and immunohistochemistry negative lymph nodes, suggesting very high sensitivity. CCAT1 is also overexpressed in 40.0% of peripheral blood samples of patients with CRC but not in healthy controls. CCAT1 is therefore a highly specific and readily detectable marker for CRC and tumor‐associated tissues.
Highlights ► Early detection of colorectal cancer (CRC). ► Peptide Nucleic Acid (PNA) probes detect a lncRNA. ► Colon cancer associated transcript 1 (CCAT1) is a lncRNA highly expressed in CRC. ► ...Detection of CCAT1 is achieved in non-fixed cells and in human biopsies.
Detection of mRNA alterations is a promising approach for identifying biomarkers as means of differentiating benign from malignant lesions. By choosing the KRAS oncogene as a target gene, two types ...of molecular beacons (MBs) based on either phosphothioated DNA (PS-DNA-MB) or peptide nucleic acid (TO-PNA-MB, where TO = thiazole orange) were synthesized and compared in vitro and in vivo. Their specificity was examined in wild-type KRAS (HT29) or codon 12 point mutation (Panc-1, SW480) cells. Incubation of both beacons with total RNA extracted from the Panc-1 cell line (fully complementary sequence) showed a fluorescent signal for both beacons. Major differences were observed, however, for single mismatch mRNA transcripts in cell lines HT29 and SW480. PS-DNA-MB weakly discriminated such single mismatches in comparison to TO-PNA-MB, which was profoundly more sensitive. Cell transfection of TO-PNA-MB with the aid of PEI resulted in fluorescence in cells expressing the fully complementary RNA transcript (Panc-1) but undetectable fluorescence in cells expressing the K-ras mRNA that has a single mismatch to the designed TO-PNA-MB (HT29). A weaker fluorescent signal was also detected in SW480 cells; however, these cells express approximately one-fifth of the target mRNA of the designed TO-PNA-MB. In contrast, PS-DNA-MB showed no fluorescence in all cell lines tested post PEI transfection. Based on the fast hybridization kinetics and on the single mismatch discrimination found for TO-PNA-MB we believe that such molecular beacons are promising for in vivo real-time imaging of endogenous mRNA with single nucleotide polymorphism (SNP) resolution.
Although thyroid nodules are common and diagnosed in over 5% of the adult population, only 5% harbor malignancy. Patients with clinically suspicious thyroid nodules need to undergo fine-needle ...aspiration biopsy (FNAB). The main limitation of FNAB remains indeterminate cytopathology. Only 20%-30% of the indeterminate nodules harbor malignancy, and therefore up to 80% of patients undergo unnecessary thyroidectomy. The aim of this study was to identify and validate a panel of microRNAs (miRNAs) that could serve as a platform for an FNAB-based diagnostic for thyroid neoplasms.
The study population included 27 consecutive patients undergoing total thyroidectomy for FNAB-based papillary thyroid cancer (n = 20) and benign disorders (n = 7). Aspiration biopsy was performed from the index lesion and from the opposite lobe normal tissue in all study patients at the time of operation. RNA was extracted from all aspiration biopsy samples. Quantitative polymerase chain reaction on a panel of previously selected miRNAs was performed. Polymerase chain reaction results were compared with final histopathology. miRNA from tumor tissues was amplified using the highest value of each miRNA expression in normal tissue as a threshold for malignancy detection.
Diagnostic characteristics were most favorable for mir-221 in differentiating benign from malignant thyroid pathology. mir-221 was overexpressed in 19 patients (p < 0.0001) with a sensitive yield of 95%. Specificity, negative and positive predictive value, and accuracy of the miRNA panel were 100%, 96%, 100%, and 98%, respectively.
miRNA quantification for differential diagnosis of thyroid neoplasms within aspiration biopsy samples is feasible and may improve the accuracy of FNAB cytology.
Long non-coding RNAs (lncRNAs) have been shown to have functional roles in cancer biology and are dys-regulated in many tumors. Colon Cancer Associated Transcript -1 (CCAT1) is a lncRNA, previously ...shown to be significantly up-regulated in colon cancer. The aim of this study is to determine expression levels of CCAT1 in gastric carcinoma (GC).
Tissue samples were obtained from patients undergoing resection for gastric carcinoma (n=19). For each patient, tumor tissue and normal appearing gastric mucosa were taken. Normal gastric tissues obtained from morbidly obese patients, undergoing laparoscopic sleeve gastrectomy served as normal controls (n=19). A human gastric carcinoma cell line (AGS) served as positive control. RNA was extracted from all tissue samples and CCAT1 expression was analyzed using quantitative real time-PCR (qRT-PCR).
Low expression of CCAT1 was identified in normal gastric mucosa samples obtained from morbidly obese patients mean Relative Quantity (RQ) = 1.95±0.4. AGS human gastric carcinoma cell line showed an elevated level of CCAT1 expression (RQ=8.02). Expression levels of CCAT1 were approximately 10.8 fold higher in GC samples than in samples taken from the negative control group (RQ=21.1±5 vs. RQ=1.95±0.4, respectively, p<0.001). Interestingly, CCAT1 expression was significantly overexpressed in adjacent normal tissues when compared to the negative control group (RQ = 15.25±2 vs. RQ=1.95±0.4, respectively, p<0.001). Tissues obtained from recurrent GC cases showed the highest expression levels (RQ = 88.8±31; p<0.001). Expression levels increased with tumor stage (T4- 36.4±15, T3- 16.1±6, T2- 4.7±1), however this did not reach statistical significance (p=0.2). There was no difference in CCAT1 expression between intestinal and diffuse type GC (RQ=22.4±7 vs. 22.4±16, respectively, p=0.9). Within the normal gastric tissue samples, no significant difference in CCAT1 expression was observed in helicobacter pylori negative and positive patients (RQ= 2.4±0.9 vs. 0.93±0.2, respectively, p=0.13).
CCAT1 is up-regulated in gastric cancer, and may serve as a potential bio-marker for early detection and surveillance.
The discovery of microRNA, a group of regulatory short RNA fragments, has added a new dimension to the diagnosis and management of neoplastic diseases. Differential expression of microRNA in a unique ...pattern in a wide range of tumor types enables researches to develop a microRNA-based assay for source identification of metastatic disease of unknown origin. This is just one example of many microRNA-based cancer diagnostic and prognostic assays in various phases of clinical research.Since colorectal cancer (CRC) is a phenotypic expression of multiple molecular pathways including chromosomal instability (CIN), micro-satellite instability (MIS) and CpG islands promoter hypermethylation (CIMP), there is no one-unique pattern of microRNA expression expected in this disease and indeed, there are multiple reports published, describing different patterns of microRNA expression in CRC.The scope of this manuscript is to provide a comprehensive review of the scientific literature describing the dysregulation of and the potential role for microRNA in the management of CRC. A Pubmed search was conducted using the following MeSH terms, "microRNA" and "colorectal cancer". Of the 493 publications screened, there were 57 papers describing dysregulation of microRNA in CRC.
Abstract Background Fine needle aspiration biopsy (FNAB) is the most commonly used diagnostic tool to differentiate benign from malignant thyroid nodules. Nevertheless, some FNAB cytology results are ...not definite. In such cases diagnostic thyroid lobectomy is performed with malignancy rate on final histopathology ranging at 15%–75%. The aim of this study was to improve on the accuracy of FNAB-based cytology by amplification of microRNAs (micro ribonucleic acids miRs) from the residual cells left in the FNAB needle after submission for cytology. Methods Residual cells were collected from the needle cup after FNAB cytology of 77 consecutive patients with thyroid nodules. miR-enriched RNA was extracted for all patients with cytology showing either follicular lesion or suspicion for malignancy ( n = 11). The expression of miR-21, -31, -146b, -187, -221, and -222 was determined using real-time polymerase chain reaction. Results were compared with final surgical histopathology. Results RNA was successfully extracted from all FNAB specimens. Five patients had FNAB cytology suspicious for malignancy. The miR panel was positive in all five (100%). Six patients had follicular lesions on FNAB. The miR panel was positive in three of four patients (75%) with confirmed malignancy and was negative in two of two (0%) patients with benign pathology results. This corresponded to a specificity of 100%, sensitivity of 88%, and accuracy of 90%. Conclusions RNA extraction from FNAB residual cells is feasible, and a miR panel amplified from the extracted RNA seems like a promising diagnostic tool in this limited number of patients.
Purpose
In computed tomography (CT) cardiovascular imaging, the numerous contrast injection protocols used to enhance structures make it difficult to gather training datasets for deep learning ...applications supporting diverse protocols. Moreover, creating annotations on noncontrast scans is extremely tedious. Recently, spectral CT’s virtual‐noncontrast images (VNC) have been used as data augmentation to train segmentation networks performing on enhanced and true‐noncontrast (TNC) scans alike, while improving results on protocols absent of their training dataset. However, spectral data are not widely available, making it difficult to gather specific datasets for each task. As a solution, we present a data augmentation workflow based on a trained image translation network, to bring spectral‐like augmentation to any conventional CT dataset.
Method
The conventional CT‐to‐spectral image translation network (HUSpectNet) was first trained to generate VNC from conventional housnfied units images (HU), using an unannotated spectral dataset of 1830 patients. It was then tested on a second dataset of 300 spectral CT scans by comparing VNC generated through deep learning (VNCDL) to their true counterparts. To illustrate and compare our workflow's efficiency with true spectral augmentation, HUSpectNet was applied to a third dataset of 112 spectral scans to generate VNCDL along HU and VNC images. Three different three‐dimensional (3D) networks (U‐Net, X‐Net, and U‐Net++) were trained for multilabel heart segmentation, following four augmentation strategies. As baselines, trainings were performed on contrasted images without (HUonly) and with conventional gray‐values augmentation (HUaug). Then, the same networks were trained using a proportion of contrasted and VNC/VNCDL images (TrueSpec/GenSpec). Each training strategy applied to each architecture was evaluated using Dice coefficients on a fourth multicentric multivendor single‐energy CT dataset of 121 patients, including different contrast injection protocols and unenhanced scans. The U‐Net++ results were further explored with distance metrics on every label.
Results
Tested on 300 full scans, our HUSpectNet translation network shows a mean absolute error of 6.70 ± 2.83 HU between VNCDL and VNC, while peak signal‐to‐noise ratio reaches 43.89 dB. GenSpec and TrueSpec show very close results regardless of the protocol and used architecture: mean Dice coefficients (DSCmean) are equal with a margin of 0.006, ranging from 0.879 to 0.938. Their performances significantly increase on TNC scans (p‐values < 0.017 for all architectures) compared to HUonly and HUaug, with DSCmean of 0.448/0.770/0.879/0.885 for HUonly/HUaug/TrueSpec/GenSpec using the U‐Net++ architecture. Significant improvements are also noted for all architectures on chest–abdominal–pelvic scans (p‐values < 0.007) compared to HUonly and for pulmonary embolism scans (p‐values < 0.039) compared to HUaug. Using U‐Net++, DSCmean reaches 0.892/0.901/0.903 for HUonly/TrueSpec/GenSpec on pulmonary embolism scans and 0.872/0.896/0.896 for HUonly/TrueSpec/GenSpec on chest–abdominal–pelvic scans.
Conclusion
Using the proposed workflow, we trained versatile heart segmentation networks on a dataset of conventional enhanced CT scans, providing robust predictions on both enhanced scans with different contrast injection protocols and TNC scans. The performances obtained were not significantly inferior to training the model on a genuine spectral CT dataset, regardless of the architecture implemented. Using a general‐purpose conventional‐to‐spectral CT translation network as data augmentation could therefore contribute to reducing data collection and annotation requirements for machine learning‐based CT studies, while extending their range of application.
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