The Expression of Circulating miR-497 and Metadherin in Hepatocellular Carcinoma: Relation to the Tumor Characteristics and Patients' Survival.

Objectives: This study aimed to evaluate the prognostic significance and relationship of miR-497 and metadherin to hepatocellular carcinoma (HCC) tumor characteristics and patients' survival. Methods: This study enrolled 120 (60 HCC patients and 60 healthy) subjects. Serum miR-497 and metadherin mRNA relative expression were analyzed by real-time quantitative reverse transcription polymerase chain reaction. The overall survival (OS) of HCC patients was assessed using the Kaplan-Meier curve and log-rank test. Results: Serum miR-497 showed statistically significant downregulation in HCC patients compared to controls (p < 0.001). Serum metadherin mRNA relative expression was significantly upregulated in HCC patients compared to controls (p < 0.001). Both serum miR-497 and metadherin mRNA expression were significantly associated with the number of tumor foci (p = 0.028 and 0.001, respectively), tumor size (p = 0.022 and <0.001, respectively), nodal metastasis (p = 0.003 and 0.003, respectively), distant metastasis (p = 0.003 and 0.003, respectively), vascular invasion (p = 0.040 and <0.001, respectively), and BCLC staging (p = 0.043 and 0.004, respectively). The overall survival was lower in patients with low miR-497 expression (p = 0.046) and in patients with high metadherin expression (p < 0.001). Conclusions: The expression levels of miR-497 showed downregulation in HCC patients, but metadherin expression showed upregulation. Both markers were inversely related and closely correlated with tumor characteristics and patients' survival.

The Prognostic Significance of MACC1 Expression in Breast Cancer and Its Relationship to Immune Cells in the Tumor Microenvironment and Patient Survival.

Breast cancer (BC) is one of the most prevalent malignancies among females worldwide. Globally, distant metastases were reported to be responsible for a large proportion of breast cancer-related deaths. The metastasis-associated colon cancer-1 (MACC1) gene was reported as a reliable biomarker for early detection of metastasis and prediction of prognosis of breast cancer. This study investigated the prognostic significance of MACC1 in breast cancer in relation to the clinicopathologic characteristics and patients' survival. Furthermore, the possible correlation between MACC1 expression and the different immune cells in the tumor microenvironment was explored. MACC1 mRNA was identified using quantitative reverse transcription polymerase chain reaction in 120 breast cancer specimens and adjacent non-cancerous tissues. MACC1 mRNA expression was significantly higher in the cancerous relative to the non-cancerous tissues (p < 0.001). High MACC1 expression was significantly associated with poor prognostic parameters, such as larger tumor size, grade III tumors, positive nodal metastasis, lymphovascular invasion, stage III tumors, and elevated Ki-67 expression. Higher MACC1 mRNA levels were positively correlated with CD163+ tumor-associated macrophages (r = 0.614, p < 0.001), and were negatively correlated with CD56+ natural killer cells (r = -0.398, p < 0.001) and CD8+ cytotoxic T lymphocytes (r = -0.323, p < 0.001). MACC1 expression was associated with poor patient overall survival (OS) and progression-free survival (PFS) (p < 0.001). Multivariate analysis suggested that MACC1 expression and the presence of lymphovascular invasion could be independent prognostic indicators for breast cancer (p = 0.015 and 0.042, respectively). In conclusion, MACC1 is highly expressed in cancerous tissues and is significantly related to poor prognostic factors, overall survival, and progression-free survival. MACC1 may influence infiltration of the immune cells in the tumor microenvironment, enhance immune escape of tumor cells, and may serve as a reliable independent prognostic factor for breast cancer.

Expression of long non-coding RNA CCHE1 in colorectal carcinoma: correlations with clinicopathological features and ERK/COX-2 pathway.

Colorectal cancer (CRC) is among the leading causes of cancer-related mortality worldwide. Compelling evidence suggests that long non-coding RNA (lncRNAs) can control carcinogenesis by regulating various aspects of cell biology. However, limited number of CRC-related lncRNAs has been well characterized. This study was undertaken to investigate the expression pattern of the novel lncRNA-CCHE1 in CRC patients and to examine its correlation with clinicopathological features, ERK/COX-2 pathway and some cell proliferation markers in order to gain biological insights on its role in CRC pathogenesis. Colon cancer specimens with their adjacent non-cancerous tissues were taken from 60 patients with primary CRC. LncRNA-CCHE1 relative expression was assessed using quantitative real-time RT-PCR. P-ERK ½ and cyclin D1 levels were estimated by ELISA. COX-2 and proliferating cell nuclear antigen (PCNA) expression were assessed immunohistochemically. lncRNA-CCHE1 expression was upregulated in CRC tissues compared to adjacent non-cancerous tissues, and was significantly associated with larger tumor size, less differentiated histology, advanced dukes' stage, positive lymph node involvement and vascular invasion. It also showed a significant positive correlation with the expression of p-ERK1/2, COX-2 as well as cyclin D1and PCNA (as markers for cell proliferation). These findings signify that lncRNA-CCHE1 is a key oncogene possibly involved in CRC development and progression by modulating ERK/COX-2 pathway and cell proliferation activity. Our study also provides a rationale for potential use of lncRNA-CCHE1 as a novel prognostic marker, and opens the door for the development of lncRNA-CCHE1-directed therapeutic approaches for CRC patients.

Computer-Aided Diagnosis System for Alzheimer's Disease Using Different Discrete Transform Techniques.

The different discrete transform techniques such as discrete cosine transform (DCT), discrete sine transform (DST), discrete wavelet transform (DWT), and mel-scale frequency cepstral coefficients (MFCCs) are powerful feature extraction techniques. This article presents a proposed computer-aided diagnosis (CAD) system for extracting the most effective and significant features of Alzheimer's disease (AD) using these different discrete transform techniques and MFCC techniques. Linear support vector machine has been used as a classifier in this article. Experimental results conclude that the proposed CAD system using MFCC technique for AD recognition has a great improvement for the system performance with small number of significant extracted features, as compared with the CAD system based on DCT, DST, DWT, and the hybrid combination methods of the different transform techniques.