Quantitative STAU2 measurement in lymphocytes for breast cancer risk assessment

The high FKBP1A expression in WBCs as a potential screening biomarker for pancreatic cancer

Given the limitation of current routine approaches for pancreatic cancer screening and detection, the mortality rate of pancreatic cancer cases is still critical. The development of blood-based molecular biomarkers for pancreatic cancer screening and early detection which provide less-invasive, high-sensitivity, and cost-effective, is urgently needed. The goal of this study is to identify and validate the potential molecular biomarkers in white blood cells (WBCs) of pancreatic cancer patients. Gene expression profiles of pancreatic cancer patients from NCBI GEO database were analyzed by CU-DREAM. Then, mRNA expression levels of three candidate genes were determined by quantitative RT-PCR in WBCs of pancreatic cancer patients (N = 27) and healthy controls (N = 51). ROC analysis was performed to assess the performance of each candidate gene. A total of 29 upregulated genes were identified and three selected genes were performed gene expression analysis. Our results revealed high mRNA expression levels in WBCs of pancreatic cancer patients in all selected genes, including FKBP1A (p < 0.0001), PLD1 (p < 0.0001), and PSMA4 (p = 0.0002). Among candidate genes, FKBP1A mRNA expression level was remarkably increased in the pancreatic cancer samples and also in the early stage (p < 0.0001). Moreover, FKBP1A showed the greatest performance to discriminate patients with pancreatic cancer from healthy individuals than other genes with the 88.9% sensitivity, 84.3% specificity, and 90.1% accuracy. Our findings demonstrated that the alteration of FKBP1A gene in WBCs serves as a novel valuable biomarker for patients with pancreatic cancer. Detection of FKBP1A mRNA expression level in circulating WBCs, providing high-sensitive, less-invasive, and cost-effective, is simple and feasible for routine clinical setting that can be applied for pancreatic cancer screening and early detection.

LaCOme: Learning the latent convolutional patterns among transcriptomic features to improve classifications

OMIC is a novel approach that analyses entire genetic or molecular profiles in humans and other organisms. It involves identifying and quantifying biological molecules that contribute to a species' structure, function, and dynamics. Finding the secrets of OMIC is like deciphering the biochemical code, but building data-driven models to mine the hidden phenotypic trait information has been a research hotspot. Transcriptome analysis is a popular biological technology for characterizing living systems' overall health, including cells and tissues. Individual transcript expression levels are known to be correlated with those of other transcripts. Nevertheless, most computational studies do not fully exploit these inter-feature correlations. Differential expression analyses, for example, assume that the expression levels of the transcripts are independent. Thus, we propose extracting these inter-feature correlations using the convolutional neural network (CNN) and transforming the transcriptomic features into a new space of convolutional transcriptomic (LaCOme) features. On most transcriptomic datasets in use, a series of comprehensive experiments have demonstrated that engineered LaCOme features outperform the original transcriptomic features in classification performances. Based on experimental results, OMIC data from biological samples could be further enriched using CNN to enhance computational analysis results. Also, feature rough screening can be used to extract valuable information from OMIC, regardless of the algorithm used to select features. It may always be better to create a novel feature than to keep the original. Furthermore, we investigated the feasibility of the feature construction method through cross-validation and independent verification, hoping to develop a more efficient and effective method.

Differential expression of immune-regulatory proteins C5AR1, CLEC4A and NLRP3 on peripheral blood mononuclear cells in early-stage non-small cell lung cancer patients

Changes in gene expression profiling of peripheral blood mononuclear cells (PBMC) appear to represent the host's response to the cancer cells via paracrine signaling. We speculated that protein expression on circulating T-lymphocytes represent T-lymphocyte trafficking before infiltration into the tumor microenvironment. The possibility of using protein expression on circulating T-lymphocytes as a biomarker to discriminate early-stage non-small cell lung cancer (NSCLC) was explored. Four independent PBMC gene expression microarray datasets (GSE12771, GSE13255, GSE20189 and GSE3934) were analyzed. We selected C5AR1, CLEC4A and NLRP3 based on their significant protein expression in tumor-infiltrating lymphocytes, but not in normal lymphoid tissue. A validation study using automated flow cytometry was conducted in 141 study participants including 76 treatment-naive early-stage non-small cell lung cancer patients (NSCLC), 12 individuals with non-malignant pulmonary diseases, and 53 healthy individuals. Median ratios of C5AR1, CLEC4A and NLRP3 specific antibody staining to CD3 positive cells in early-stage NSCLC patients compared to healthy controls were 0.014 [0-0.37] vs. 0.01 [0-0.07, p = 0.13], 0.03 [0-0.87] vs. 0.02 [0-0.13, p = 0.10] and 0.19 [0-0.60] vs. 0.09 [0.02-0.31, p < 0.0001], respectively. Median fluorescence intensity (MFI) of CD3⁺C5AR1⁺, CD3⁺CLEC4A⁺ and CD3⁺NLRP3⁺ expression in early-stage NSCLC patients compared to healthy volunteers was 185 [64.2-4801] vs. 107.5 [27-229, p < 0.0001], 91.2 [42.4-2355] vs. 71.25 [46.2-103, p = 0.0005], and 1585 [478-5224] vs. 758.5 [318-1976, p < 0.0001], respectively. NLRP3:CD3 ratio, CD3⁺C5AR1⁺, CD3⁺CLEC4A⁺ and CD3⁺NLRP3⁺ MFI were significantly higher in early-stage NSCLC than healthy volunteers with an area under the ROC curve of 0.69-0.76. The CD3⁺NLRP3⁺ MFI provided the most distinguishable expression at 71.5% sensitivity and 70% specificity. Furthermore, CD3⁺NLRP3⁺ MFI potentially discriminated between early-stage NSCLC from malignant-mimic inflammation and infection pulmonary disease. Further validation in various pulmonary inflammatory disease might be warranted. Our proof-of-principle findings strengthen the hypothesis that malignancies generate distinctive protein expression fingerprints on circulating T-lymphocytes.

Upregulation of p16INK4A in Peripheral White Blood Cells as a Novel Screening Marker for Colorectal Carcinoma

OBJECTIVE

Screening of colorectal cancer (CRC) is important for the early detection. CRC is relating to aging and immuno-senescence. One such senescent marker is p16INK4A expression in immune cells. The objective of the study is to investigate the protein expression of p16INK4A in peripheral white blood cells as a screening marker for colorectal cancer.

METHODS

A case-control studies were conducted. Cases were patients with colorectal cancer and controls were matched with cases based on age and sex. Peripheral blood was collected from patients and controls and the protein p16INK4A was measured with immunofluorescent techniques. The p16INK4A levels from cases and controls were evaluated using ROC analysis to be used as a screening marker in CRC patients. Mean fluorescent intensity of p16INK4A of cases and controls were analyzed in CD45+, CD3+ or CD14+ cells. The p16INK4A levels of cases were also correlated with clinical data.

RESULT

Statistically significant increased expression of p16INK4A levels were found in cases compared to controls. p16INK4A in peripheral immune cells had 78% sensitivity and 71% specificity which can possibly be used as a diagnosis tool for colorectal cancer. P16INK4A-positive cell percentage and mean florescent intensity were significantly higher in CD45+ cells, CD3 positive cells and CD14 positive cells. No significant correlation was observed with the clinical data and p16INK4A level of CRC patients.

CONCLUSION

The significant increase of p16 INK4A expression level in peripheral immune cells represents potential for use as a CRC screening marker.

The expression of FLNA and CLU in PBMCs as a novel screening marker for hepatocellular carcinoma

Early detection improves survival and increases curative probability in hepatocellular carcinoma (HCC). Peripheral blood mononuclear cells (PBMCs) can provide an inexpensive, less-invasive and highly accurate method. The objective of this study is to find the potential marker for HCC screening, utilizing gene expression of the PBMCs. Data from the NCBI GEO database of gene expression in HCC patients and healthy donor's PBMCs was collected. As a result, GSE 49515 and GSE 58208 were found. Using both, a statistical significance test was conducted in each gene expression of each data set which resulted in 187 genes. We randomized three selected genes (FLNA, CAP1, and CLU) from the significant p-value group (p-values < 0.001). Then, a total of 76 healthy donors, 153 HCC, 20 hepatic fibrosis, 20 non-alcoholic fatty liver were collected. Quantitative RT-PCR (qRT-PCR) was performed in cDNA from all blood samples from the qRT-PCR, The Cycle threshold (Ct) value of FLNA, CLU, CAP1 of HCC group (28.47 ± 4.43, 28.01 ± 3.75, 29.64 ± 3.90) were lower than healthy group (34.23 ± 3.54, 32.90 ± 4.15, 32.18 ± 5.02) (p-values < 0.0001). The accuracy, sensitivity and specificity of these genes as a screening tool were: FLNA (80.8%, 88.0%, 65.8%), CLU (63.4%, 93.3%, 31.3%), CAP1 (67.2%, 83.3%, 39.1%). The tests were performed in two and three gene combinations. Results demonstrated high accuracy of 86.2%, sensitivity of 85% and specificity of 88.4% in the FLNA and CLU combination. Furthermore, after analyzed using hepatic fibrosis and non-alcoholic fatty liver as a control, the FLNA and CLU combination is shown to have accuracy of 76.9%, sensitivity of 77.6% and specificity of 75%. Also, we founded that our gene combination performs better than the current gold standard for HCC screening. We concluded that FLNA and CLU combination have high potential for being HCC novel markers. Combined with current tumor markers, further research of the gene’s expression might help identify more potential markers and improve diagnosis methods.