Computational analysis of mRNA expression profiles identifies microRNA-29a/c as predictor of colorectal cancer early recurrence

Colorectal cancer (CRC) is one of the leading malignant cancers with a rapid increase in incidence and mortality. The recurrences of CRC after curative resection are sometimes unavoidable and often take place within the first year after surgery. MicroRNAs may serve as biomarkers to predict early recurrence of CRC, but identifying them from over 1,400 known human microRNAs is challenging and costly. An alternative approach is to analyze existing expression data of messenger RNAs (mRNAs) because generally speaking the expression levels of microRNAs and their target mRNAs are inversely correlated. In this study, we extracted six mRNA expression data of CRC in four studies (GSE12032, GSE17538, GSE4526 and GSE17181) from the gene expression omnibus (GEO). We inferred microRNA expression profiles and performed computational analysis to identify microRNAs associated with CRC recurrence using the IMRE method based on the MicroCosm database that includes 568,071 microRNA-target connections between 711 microRNAs and 20,884 gene targets. Two microRNAs, miR-29a and miR-29c, were disclosed and further meta-analysis of the six mRNA expression datasets showed that these two microRNAs were highly significant based on the Fisher p-value combination (p = 9.14 × 10(-9) for miR-29a and p = 1.14 × 10(-6) for miR-29c). Furthermore, these two microRNAs were experimentally tested in 78 human CRC samples to validate their effect on early recurrence. Our empirical results showed that the two microRNAs were significantly down-regulated (p = 0.007 for miR-29a and p = 0.007 for miR-29c) in the early-recurrence patients. This study shows the feasibility of using mRNA profiles to indicate microRNAs. We also shows miR-29a/c could be potential biomarkers for CRC early recurrence.     

Identification and verification of heat shock protein 60 as a potential serum marker for colorectal cancer

Colorectal cancer (CRC) is a major public health issue worldwide, and novel tumor markers may contribute to its efficient management by helping in early detection, prognosis or surveillance of disease. The aim of our study was to identify new serum biomarkers for CRC, and we followed a phased biomarker discovery and validation process to obtain an accurate preliminary assessment of potential clinical utility. We compared colonic tumors and matched normal tissue from 15 CRC patients, using two-dimensional difference gel electrophoresis (2D-DIGE), and identified 17 proteins that had significant differential expression. These results were further confirmed by western blotting for heat shock protein (HSP) 60, glutathione-S-transferase Pi, α-enolase, T-complex protein 1 subunit β, and leukocyte elastase inhibitor, and by immunohistochemistry for HSP60. Using mAbs raised against HSP60, we developed a reliable (precision of 5-15%) and sensitive (0.3 ng·mL(-1)) immunoassay for the detection of HSP60 in serum. Elevated levels of HSP60 were found in serum from CRC patients in two independent cohorts; the receiver-operating characteristic curve obtained in 112 patients with CRC and 90 healthy controls had an area under the curve (AUC) of 0.70, which was identical to the AUC of carcinoembryonic antigen. Combination of serum markers improved clinical performance: the AUC of a three-marker logistic regression model combining HSP60, carcinoembryonic antigen and carbohydrate antigen 19-9 reached 0.77. Serum HSP60 appeared to be more specific for late-stage CRC; therefore, future studies should evaluate its utility for determining prognosis or monitoring therapy rather than early detection.     

Proteomic Discovery of Plasma Protein Biomarkers and Development of Models Predicting Prognosis of High-Grade Serous Ovarian Carcinoma

Ovarian cancer is one of the most lethal female cancers. For accurate prognosis prediction, this study aimed to investigate novel, blood-based prognostic biomarkers for high-grade serous ovarian carcinoma (HGSOC) using mass spectrometry–based proteomics methods. We conducted label-free liquid chromatography–tandem mass spectrometry using frozen plasma samples obtained from patients with newly diagnosed HGSOC (n = 20). Based on progression-free survival (PFS), the samples were divided into two groups: good (PFS ≥18 months) and poor prognosis groups (PFS <18 months). Proteomic profiles were compared between the two groups. Referring to proteomics data that we previously obtained using frozen cancer tissues from chemotherapy-naïve patients with HGSOC, overlapping protein biomarkers were selected as candidate biomarkers. Biomarkers were validated using an independent set of HGSOC plasma samples (n = 202) via enzyme-linked immunosorbent assay (ELISA). To construct models predicting the 18-month PFS rate, we performed stepwise selection based on the area under the receiver operating characteristic curve (AUC) with 5-fold cross-validation. Analysis of differentially expressed proteins in plasma samples revealed that 35 and 61 proteins were upregulated in the good and poor prognosis groups, respectively. Through hierarchical clustering and bioinformatic analyses, GSN, VCAN, SND1, SIGLEC14, CD163, and PRMT1 were selected as candidate biomarkers and were subjected to ELISA. In multivariate analysis, plasma GSN was identified as an independent poor prognostic biomarker for PFS (adjusted hazard ratio, 1.556; 95% confidence interval, 1.073–2.256; p = 0.020). By combining clinical factors and ELISA results, we constructed several models to predict the 18-month PFS rate. A model consisting of four predictors (FIGO stage, residual tumor after surgery, and plasma levels of GSN and VCAN) showed the best predictive performance (mean validated AUC, 0.779). The newly developed model was converted to a nomogram for clinical use. Our study results provided insights into protein biomarkers, which might offer clues for developing therapeutic targets.     

Matrix metalloproteinase 2 and tissue inhibitor of matrix metalloproteinases 2 in the diagnosis of colorectal adenoma and cancer patients

The aim of the study was to assess the importance of the measurement of matrix metalloproteinase 2 (MMP-2) and tissue inhibitor of matrix metalloproteinases 2 (TIMP-2) in patients with colorectal cancer (CRC) in relation to clinicopathological features of tumor and patients' survival. Additionally, we determined serum MMP-2 and TIMP-2 in colorectal adenoma (CA) patients and healthy controls and compared them with tumor markers, CEA and CA 19-9. The serum levels of MMP-2 and TIMP-2 in 91 CRC patients, 28 CA subjects and 91 healthy controls were determined by ELISA method, but concentrations of CEA and CA 19-9 using MEIA method. Nonparametric statistical analyses were used. Serum levels of MMP-2 and TIMP-2 were significantly lower in CRC patients than in healthy subjects and decreased with tumor stage. Additionally, MMP-2 concentrations were significantly lower in patients with CRC than in CA group. Diagnostic sensitivity of TIMP-2 (59%) was the highest among biomarkers tested and increased in combined use with CEA (79%). Moreover, the area under ROC curve (AUC) of TIMP-2 was larger than AUC of MMP-2 in differentiation between CRC and healthy subjects, but lower than AUC of matrix metalloproteinase 2 in differentiation between colorectal cancer and adenoma. Our findings suggest clinical usefulness of TIMP-2 as a biomarker in the diagnosis of CRC, especially in combination with CEA. However, further investigation is necessary.     

Identification of potential miRNA biomarkers for traumatic osteonecrosis of femoral head

Traumatic osteonecrosis of femoral head (TONFH) is a common orthopedic disease caused by physical injury in hip. However, the unclear pathogenesis mechanism of TONFH and lacking of simple noninvasive early diagnosis method cause the necessity of hip replacement for most patients with TONFH. In this study, we aimed to identify circulating microRNAs (miRNAs) by integrated bioinformatics analyses as potential biomarker of TONFH. mRNA expression profiles were downloaded from the Gene Expression Omnibus database. Then we combined two miRNA screen methods: Weighted gene co-expression network analysis and fold change based differentially expressed miRNAs analysis. As a result, we identified 14 key miRNAs as potential biomarkers for TONFH. Besides, 302 target genes of these miRNAs were obtained and the miRNA–mRNA interaction network was constructed. Furthermore, the results of Kyoto Encyclopedia of Gene and Genome pathway analysis, Gene Ontology function analysis, protein–protein interaction (PPI) network analysis and PPI network module analysis showed close correlation between these 14 key miRNAs and TONFH. Then we established receiver operating characteristic curves and identified 6-miRNA signature with highly diagnosis value including miR-93-5p (area under the curve [AUC] = 0.93), miR-1324 (AUC = 0.92), miR-4666a-3p (AUC = 0.92), miR-5011-3p (AUC = 0.92), and miR-320a (AUC = 0.89), miR-185-5p (AUC = 0.89). Finally, the results of quantitative real-time polymerase chain reaction confirmed the significantly higher expression of miR-93-5p and miR-320a in the serum of patients with ONFH. These circulating miRNAs could serve as candidate early diagnosis markers and potential treatment targets of TONFH.     

A model free approach to combining biomarkers

For most diseases, single biomarkers do not have adequate sensitivity or specificity for practical purposes. We present an approach to combine several biomarkers into a composite marker score without assuming a model for the distribution of the predictors. Using sufficient dimension reduction techniques, we replace the original markers with a lower-dimensional version, obtained through linear transformations of markers that contain sufficient information for regression of the predictors on the outcome. We combine the linear transformations using their asymptotic properties into a scalar diagnostic score via the likelihood ratio statistic. The performance of this score is assessed by the area under the receiver-operator characteristics curve (ROC), a popular summary measure of the discriminatory ability of a single continuous diagnostic marker for binary disease outcomes. An asymptotic chi-squared test for assessing individual biomarker contribution to the diagnostic score is also derived.     

Serum MicroRNAs as Potential Biomarkers for Early Diagnosis of Hepatitis C Virus-Related Hepatocellular Carcinoma in Egyptian Patients

Circulating microRNAs are deregulated in liver fibrosis and hepatocellular carcinoma (HCC) and are candidate biomarkers. This study investigated the potential of serum microRNAs; miR-19a, miR-296, miR-130a, miR-195, miR-192, miR-34a, and miR-146a as early diagnostic biomarkers for hepatitis C virus (HCV)-related HCC. As how these microRNAs change during liver fibrosis progression is not clear, we explored their serum levels during fibrosis progression in HCV-associated chronic liver disease (CLD) and if they could serve as non-invasive biomarkers for fibrosis progression to HCC. 112 Egyptian HCV-HCC patients, 125 non-malignant HCV-CLD patients, and 42 healthy controls were included. CLD patients were subdivided according to Metavir fibrosis-scoring. Serum microRNAs were measured by qRT-PCR custom array. Serum microRNAs were deregulated in HCC versus controls, and except miR-130a, they were differentially expressed between HCC and CLD or late fibrosis (F3-F4) subgroup. Serum microRNAs were not significantly different between individual fibrosis-stages or between F1-F2 (early/moderate fibrosis) and F3-F4. Only miR-19a was significantly downregulated from liver fibrosis (F1-F3) to cirrhosis (F4) to HCC. Individual microRNAs discriminated HCC from controls, and except miR-130a, they distinguished HCC from CLD or F3-F4 patients by receiver-operating-characteristic analysis. Multivariate logistic analysis revealed a panel of four microRNAs (miR-19a, miR-195, miR-192, and miR-146a) with high diagnostic accuracy for HCC (AUC = 0.946). The microRNA panel also discriminated HCC from controls (AUC = 0.949), CLD (AUC = 0.945), and F3-F4 (AUC = 0.955). Studied microRNAs were positively correlated in HCC group. miR-19a and miR-34a were correlated with portal vein thrombosis and HCC staging scores, respectively. In conclusion, studied microRNAs, but not miR-130a, could serve as potential early biomarkers for HCC in high-risk groups, with miR-19a as a biomarker for liver fibrosis progression to cirrhosis to HCC. We identified a panel of four serum microRNAs with high accuracy in HCC diagnosis. Additional studies are required to confirm this panel and test its prognostic significance.     

Serum miR‐92a‐1 is a novel diagnostic biomarker for colorectal cancer

Colorectal cancer (CRC) is one of the most common cancers worldwide, with high mortality. Abnormally expressed microRNAs (miRNAs) are considered novel biomarkers in cancer diagnosis. The aim of this study was to investigate the diagnostic value of miR‐92a‐1 in patients with CRC. Serum samples were collected from 148 patients pathologically diagnosed with CRC and 68 gender‐ and age‐matched healthy volunteers. Quantitative real‐time polymerase chain reaction (qRT‐PCR) was used to measure serum miR‐92a‐1 level. Relationship between miR‐92a‐1 and clinicopathological features of CRC cases was analysed via chi‐square test. Receiver operating characteristic (ROC) curve was plotted to estimate the diagnostic value of miR‐92a‐1 in CRC. Serum miR‐92a‐1 was significantly up‐regulated in CRC patients compared with healthy individuals (P < .001). Moreover, miR‐92a‐1 expression was correlated with TNM stage (P = .02), histological stage (P = .003), lymph node metastasis (P = .003) and distant metastasis (P < .001). ROC analysis showed that the area under the ROC curve (AUC) was 0.914, suggesting high diagnostic accuracy of miR‐92a‐1 in ROC. The optimal cut‐off value was 1.485, with a sensitivity of 81.8% and a specificity of 95.6%. MiR‐92a‐1 is increased in CRC patients and correlated with aggressive clinical characteristics. Serum miR‐92a‐1 may be a potential diagnostic biomarker for CRC.     

Identifying potential DNA methylation markers in early-stage colorectal Cancer

Colorectal cancer (CRC) is the second leading malignancy worldwide. Accurate screening is pivotal to early CRC detection, yet current screening modality involves invasive colonoscopy while non-invasive FIT tests have limited sensitivity. We applied a DNA methylation assay to identify biomarkers for early-stage CRC detection, risk stratification and precancerous lesion screening at tissue level. A model of biomarkers SFMBT2, ITGA4, THBD and ZNF304 showed 96.1% sensitivity and 87.0% specificity in CRC detection, with 100.0% sensitivity for advanced precancerous lesion and stage I CRC. Performances were further validated with TCGA data set, which showed a consistent AUC of 0.99 and exhibited specificity against other cancer types. KCNJ12, VAV3-AS1 and EVC were further identified for stage stratification (stage 0-I versus stage II-IV), with AUC of 0.87, 83.0% sensitivity and 71.2% specificity. Additionally, dual markers of NEUROD1 and FAM72C showed 83.2% sensitivity and 77.4% specificity in differing non-advanced precancerous lesions from inflammatory bowel diseases.     

Molecular Predictors of Long-Term Survival in Glioblastoma Multiforme Patients

Glioblastoma multiforme (GBM) is the most common and aggressive adult primary brain cancer, with <10% of patients surviving for more than 3 years. Demographic and clinical factors (e.g. age) and individual molecular biomarkers have been associated with prolonged survival in GBM patients. However, comprehensive systems-level analyses of molecular profiles associated with long-term survival (LTS) in GBM patients are still lacking. We present an integrative study of molecular data and clinical variables in these long-term survivors (LTSs, patients surviving >3 years) to identify biomarkers associated with prolonged survival, and to assess the possible similarity of molecular characteristics between LGG and LTS GBM. We analyzed the relationship between multivariable molecular data and LTS in GBM patients from the Cancer Genome Atlas (TCGA), including germline and somatic point mutation, gene expression, DNA methylation, copy number variation (CNV) and microRNA (miRNA) expression using logistic regression models. The molecular relationship between GBM LTS and LGG tumors was examined through cluster analysis. We identified 13, 94, 43, 29, and 1 significant predictors of LTS using Lasso logistic regression from the somatic point mutation, gene expression, DNA methylation, CNV, and miRNA expression data sets, respectively. Individually, DNA methylation provided the best prediction performance (AUC = 0.84). Combining multiple classes of molecular data into joint regression models did not improve prediction accuracy, but did identify additional genes that were not significantly predictive in individual models. PCA and clustering analyses showed that GBM LTS typically had gene expression profiles similar to non-LTS GBM. Furthermore, cluster analysis did not identify a close affinity between LTS GBM and LGG, nor did we find a significant association between LTS and secondary GBM. The absence of unique LTS profiles and the lack of similarity between LTS GBM and LGG, indicates that there are multiple genetic and epigenetic pathways to LTS in GBM patients.     

Comparative proteomic analysis for the insoluble fractions of colorectal cancer patients

We used label-free quantitative proteomics with the insoluble fractions from colorectal cancer (CRC) patients to gain further insight into the utility of profiling altered protein expression as a potential biomarker for cancer. The insoluble fractions were prepared from paired tumor/normal biopsies from 13 patients diagnosed with CRC (stages I to IV). Fifty-six proteins identified in data pooled from the 13 cases were differentially expressed between the tumor and adjacent normal tissue. The connections between these proteins are involved in reciprocal networks related to tumorigenesis, cancer incidence based on genetic disorder, and skeletal and muscular disorders. To assess their potential utility as biomarkers, the relative expression levels of the proteins were validated using personal proteomics and a heat map to compare five individual CRC samples with five normal tissue samples. Further validation of a panel of proteins (KRT5, JUP, TUBB, and COL6A1) using western blotting confirmed the differential expression. These proteins gave specific network information for CRC, and yielded a panel of novel markers and potential targets for treatment. It is anticipated that the experimental approach described here will increase our understanding of the membrane environment in CRC, which may provide direction for making diagnoses and prognoses through molecular biomarker targeting.     

Expression profiling of more than 3500 proteins of MSS-type colorectal cancer by stable isotope labeling and mass spectrometry

An efficient means of identifying protein biomarkers is essential to proper cancer management. A well-characterized proteome resource holds special promise for the discovery of novel biomarkers. However, quantification of the differences between physiological conditions together with deep down profiling has become increasingly challenging in proteomics. Here, we perform expression profiling of the colorectal cancer (CRC) proteome by stable isotope labeling and mass spectrometry. Quantitative analysis included performing mTRAQ and cICAT labeling in a pooled sample of three microsatellite stable (MSS) type CRC tissues and a pooled sample of their matched normal tissues. We identified and quantified a total of 3688 proteins. Among them, 1487 proteins were expressed differentially between normal and cancer tissues by higher than 2-fold; 1009 proteins showed increased expression in cancer tissue, whereas 478 proteins showed decreased expression. Bioinformatic analysis revealed that our data were largely consistent with known CRC relevant signaling pathways, such as the Wnt/β-catenin, caveolar-mediated endocytosis, and RAN signaling pathways. Mitochondrial dysfunction, known as the Waburg hypothesis, was also confirmed. Therefore, our data showing alterations in the proteomic profile of CRC constitutes a useful resource that may provide insights into tumor progression with later goal of identifying biologically and clinically relevant marker proteins. This article is part of a Special Issue entitled: Proteomics: The clinical link.     

Potentially Functional SNPs (pfSNPs) as Novel Genomic Predictors of 5-FU Response in Metastatic Colorectal Cancer Patients

5-Fluorouracil (5-FU) and its pro-drug Capecitabine have been widely used in treating colorectal cancer. However, not all patients will respond to the drug, hence there is a need to develop reliable early predictive biomarkers for 5-FU response. Here, we report a novel potentially functional Single Nucleotide Polymorphism (pfSNP) approach to identify SNPs that may serve as predictive biomarkers of response to 5-FU in Chinese metastatic colorectal cancer (CRC) patients. 1547 pfSNPs and one variable number tandem repeat (VNTR) in 139 genes in 5-FU drug (both PK and PD pathway) and colorectal cancer disease pathways were examined in 2 groups of CRC patients. Shrinkage of liver metastasis measured by RECIST criteria was used as the clinical end point. Four non-responder-specific pfSNPs were found to account for 37.5% of all non-responders (P<0.0003). Five additional pfSNPs were identified from a multivariate model (AUC under ROC = 0.875) that was applied for all other pfSNPs, excluding the non-responder-specific pfSNPs. These pfSNPs, which can differentiate the other non-responders from responders, mainly reside in tumor suppressor genes or genes implicated in colorectal cancer risk. Hence, a total of 9 novel SNPs with potential functional significance may be able to distinguish non-responders from responders to 5-FU. These pfSNPs may be useful biomarkers for predicting response to 5-FU.     

Hypermethylated and downregulated MEIS2 are involved in stemness properties and oxaliplatin-based chemotherapy resistance of colorectal cancer

The resistance against oxaliplatin (L-OHP) based regimens remains a major obstacle for its efficient usage in treating metastatic colorectal cancer (mCRC). In this study, we performed weighted gene coexpression network analysis (WGCNA) to systematically screen the relevant hub genes for L-OHP resistance using the raw microarray data of 30 consecutive mCRC samples from our earlier study (GSE69657). The results were further confirmed through datasets from Gene Expression Omnibus (GEO). From L-OHP resistance module, nine genes in both the coexpression and protein–protein interaction networks were chosen as hub genes. Among these genes, Meis Homeobox 2 (MEIS2) had the highest correlation with L-OHP resistance (r = −0.443) and was deregulated in L-OHP resistant tissues compared with L-OHP sensitive tissues in both our own dataset and GSE104645 testing dataset. The receiver operating characteristic curve validated that MEIS2 had a good ability in predicting L-OHP response in both our own dataset (area under the curve [AUC] = 0.802) and GSE104645 dataset (AUC = 0.746). Then, the down expression of MEIS2 was observed in CRC tissue compared with normal tissue in 12 GEO-sourced datasets and The Cancer Genome Atlas (TCGA) and was correlated with poor event-free survival. Furthermore, analyzing methylation data from TCGA showed that MEIS2 had increased promoter hypermethylation. In addition, MEIS2 expression was significantly decreased in CRC stem cells compared with nonstem cells in two GEO datasets (GSE14773 and GSE24747). Further methylation analysis from GSE104271 demonstrated that CRC stem cells had higher MEIS2 promoter methylation levels in cg00366722 and cg00610348 sites. Gene set enrichment analysis showed that MEIS2 might be involved in the Wnt/β-catenin pathway. In the overall view, MEIS2 had increased promoter hypermethylation and was downregulated in poor L-OHP response mCRC tissues. MEIS2 might be involved in the Wnt/β-catenin pathway to maintain CRC stemness, which leads to L-OHP resistance.     

Proteomic analysis of colorectal cancer: prefractionation strategies using two-dimensional free-flow electrophoresis

This review deals with the application of a new prefractionation tool, free-flow electrophoresis (FFE), for proteomic analysis of colorectal cancer (CRC). CRC is a leading cause of cancer death in the Western world. Early detection is the single most important factor influencing outcome of CRC patients. If identified while the disease is still localized, CRC is treatable. To improve outcomes for CRC patients there is a pressing need to identify biomarkers for early detection (diagnostic markers), prognosis (prognostic indicators), tumour responses (predictive markers) and disease recurrence (monitoring markers). Despite recent advances in the use of genomic analysis for risk assessment, in the area of biomarker identification genomic methods alone have yet to produce reliable candidate markers for CRC. For this reason, attention is being directed towards proteomics as a complementary analytical tool for biomarker identification. Here we describe a proteomics separation tool, which uses a combination of continuous FFE, a liquid-based isoelectric focusing technique, in the first dimension, followed by rapid reversed-phase HPLC (1-6 min/analysis) in the second dimension. We have optimized imaging software to present the FFE/RP-HPLC data in a virtual 2D gel-like format. The advantage of this liquid based fractionation system over traditional gel-based fractionation systems is the ability to fractionate large quantity protein samples. Unlike 2D gels, the method is applicable to both high-M(r) proteins and small peptides, which are difficult to separate, and in the case of peptides, are not retained in standard 2D gels.     

Identification and Validation of Potential Biomarkers for the Detection of Dysregulated microRNA by qPCR in Patients with Colorectal Adenocarcinoma

Colorectal cancer represents a lethal disease that has raised concern and has attracted significant attention. Adenocarcinoma is the most common type of colorectal cancer (CRC). MicroRNAs are thought to be potential biomarkers of CRC. Many researchers have focused on the expression pattern of miRNAs in CRC. However, previous studies did not pay particular attention to the effects of the degree of differentiation of the cancer with respect to the miRNA expression profile. First, this study compared the expression level of 1547 miRNAs by qRT-PCR in Colorectal adenocarcinoma tissues to that in paired normal tissues. In all, 93 miRNAs were identified that were significantly dysregulated in Colorectal adenocarcinoma relative to normal tissues (P<0.05). Then, we analyzed their potential as cancer biomarkers by ROC analysis, and the result revealed that three miRNAs with high sensitivity and specificity are suitable as biomarkers for the diagnosis of CRC (the value of the AUC was greater than 0.7). Interestingly, previous reports of 23 of these miRNAs have been scarce. Furthermore, we wanted to analyze the difference between well- and moderately differentiated cancers, and as expected, 58 miRNAs showed significant dysregulation. Importantly, 32 miRNAs were able to not only distinguish cancer tissues from normal tissues, but they were also able to identify well- and moderately differentiated cancers. In conclusion, the degree of differentiation has an important influence on the miRNA expression pattern. To avoid misdiagnoses and missed diagnoses, tumors of different degrees of differentiation should be treated differently when miRNAs are used as cancer biomarkers.     

Identification of miRNAs Differentially Expressed in Clinical Stages of Human Colorectal Carcinoma—An Investigation in Guangzhou,China

Aberrant expression of microRNAs (miRNAs) has been implicated in human cancer, including colorectal cancer (CRC). Such dysregulated miRNAs may have potential as diagnostic markers or therapeutic targets. However, the nature of an association between these miRNAs and clinical stages of CRC is still not clear. To this end, we performed a miRNA profiling of 1547 distinct human miRNAs using 31 samples of tumor and paired normal mucosa obtained from 31 CRC patients. Based on statistical analyses of profiling data, we identified 569 miRNAs that were significantly dysregulated in CRC relative to normal tissues (P<0.05). Among the 569 dysregulated miRNAs, downregulation of 17 was associated with stages II, III, and IV colon and rectal cancers (separate or combined), according to our criteria. We also assessed the potential of these dysregulated miRNAs as diagnostic biomarkers for CRC patients who were without metastasis, and the value of the dysregulated miRNAs for predicting metastasis, lymph node and distant. Their distinct expression patterns in colon and rectal cancers were also examined. Although our findings cannot be immediately applied toward clinical diagnosis, our new study model for determining and assessing the biomarker potential of dysregulated miRNAs should be useful in further research in detection of human CRC.     

Multiplexed DNA Methylation Analysis in Colorectal Cancer Using Liquid Biopsy and Its Diagnostic and Predictive Value

Early diagnosis of colorectal cancer (CRC) is of high importance as prognosis depends on tumour stage at the time of diagnosis. Detection of tumour-specific DNA methylation marks in cfDNA has several advantages over other approaches and has great potential for solving diagnostic needs. We report here the identification of DNA methylation biomarkers for CRC and give insights in our methylation-sensitive restriction enzyme coupled qPCR (MSRE-qPCR) system. Targeted microarrays were used to investigate the DNA methylation status of 360 cancer-associated genes. Validation was done by qPCR-based approaches. A focus was on investigating marker performance in cfDNA from 88 patients (44 CRC, 44 controls). Finally, the workflow was scaled-up to perform 180plex analysis on 110 cfDNA samples, to identify a DNA methylation signature for advanced colonic adenomas (AA). A DNA methylation signature (n = 44) was deduced from microarray experiments and confirmed by quantitative methylation-specific PCR (qMSP) and by MSRE-qPCR, providing for six genes’ single areas under the curve (AUC) values of >0.85 (WT1, PENK, SPARC, GDNF, TMEFF2, DCC). A subset of the signatures can be used for patient stratification and therapy monitoring for progressed CRC with liver metastasis using cfDNA. Furthermore, we identified a 35-plex classifier for the identification of AAs with an AUC of 0.80.