Early Diagnosis Of Colon Cancer Research Articles

Precision Oncology: Circulating Microvesicles as New Biomarkers in a Very Early Stage of Colorectal Cancer.

The release of microvesicles (MVs) is an essential phenomenon for inter-cellular signaling in health and disease. The role of MVs in cancer is multidimensional and includes cancer cell survival, proliferation, and invasion. In this prospective study, we analyzed MV levels in colorectal cancer patients and assessed the importance of MV release in early-stage colorectal cancer and survival. This study included 98 patients and 15 controls. The characterization of MVs from human plasma was performed by flow cytometry using monoclonal antibodies. The levels of total MVs and MUC-1-positive, tissue factor (TF)-positive, and endothelial cell-derived MVs (EMVs) were statistically significantly higher in the colon cancer patients than in the controls (p < 0.001). Furthermore, the subgroup of patients with very early-stage colorectal cancer also had statistically significant differences in the levels of the abovementioned MVs compared to the controls (p < 0.01). Highly differentiated tumors had lower levels of MUC-1-positive MVs (p < 0.02), EMVs (p < 0.002), and EMV/TF combinations (p < 0.001) versus those with tumors with low/intermediate differentiation. Our data demonstrate that the analysis of circulating MV levels in plasma could possibly become a tool for the early diagnosis of colon cancer at a very early stage of the disease.

The role of molecular biomarker CDKN-2A in early diagnosis of colorectal cancer.

10581 Background: Polyps are considered as obligate precancerous disease. The percentage of malignization ranges from 20% in single polyps to 95% in diffuse polyposis. Current research examines the results of the early diagnosis of colon cancer by using the biomarker CDKN2A for development of molecular-biological methods of prognostication. The correlation with the molecular-biological indicators were studied. It is also planned to develop an effective system for development prognostication of CRC and the formation of "risk groups" for evidence-based counselling for families which are highly predisposed to colorectal cancer. Methods: Investigations were carried out in 30 patients during the period 2016-2022, the study included patients with single polyps, multiple polyps, polyposis of the colon, as well as patients without a history of either familial or sporadic cancer. 12 patients had multiple polyps, 9 polyposis and 9 single colon polyps. The levels of methylation of the gene CDKN2A were determined in 10 patients. The material for the isolation of DNA was a serum obtained from venous blood, and biopsy material. DNA extraction was held according to standards. After the conversion of DNA methyl-specific PCR was done. Results: Among 30 patients with polyps and polyposis of the colon, serum gene CDKN2A methylated DNA was detected in 10 patients. In 4 patients malignization of the polyp was determined. If patients without malignization surgical intervention was limited with resection of the intestine without lymph dissection. In 20 patients the test result was negative. We did not perform surgical intervention to these patients and they were sent to dynamic observation. Thus, the diagnostic efficacy of detecting colorectal cancer in early diagnosis of by means of methylated DNA of the gene CDKN2A was - 33.3%. Conclusions: If there is a positive test for methylation of the CDKN2A-gene, the risk of colorectal cancer is considered very high and these patients underwent surgical treatment. Application of the molecular biological marker CDKN2A may be an effective method in early diagnosis of malignization of polyps.

Ratiometric SERS-based assay with "sandwich" structure for detection of serotonin.

A ratiometric nanoensemble-functionalized Surface-Enhanced Raman Spectroscopy (SERS) chip is proposed and an ultrasensitive "sandwich" structure introduced for the detection of 5-HT to achieve early diagnosis of colon cancer. The SERS-based chip contains core-shell SERS active substrates coded by different Raman tags with Raman-silent region peaks (Au@EBP@Au NR arrays and Au@MBN@Ag NPs) and then identify-function molecule modification to construct the "sandwich" structure (Au@EBP@Au NR arrays/5-HT/Au@MBN@Ag NPs). Au@EBP@Au NR arrays showed excellent SERS performance, including good uniformity with an RSD of 5.53% and an enhancement factor (EF) of 2.13 × 107. The intensity ratio of the peaks in the Raman silent region was proportional to the concentration of 5-HT in the range 5 × 10-7-1 × 10-3M, with a detection limit (LOD) of 4.9 × 10-9M. Excellent assay accuracy was also demonstrated, with recoveries in the range 96.80% to 104.96%. Finally, we found that 5-HT expression levels in normal human sera were much lower than those in colon cancer patients by using a SERS-based chip for determination of the concentration of 5-HT in clinical colon cancer serum. This result suggested that the proposed approach has potential for detecting 5-HT by ratiometric SERS-based chips for early diagnosis of colon cancer.

TCP-1, a novel peptide to diagnose early colon cancer

A nine cyclic peptide (TCP-1) showed excellent specificity for colon cancer. TCP-1 binds with human tumor tissues at early stages and mice tumor with diameters of 1-4 mm, suggesting that TCP-1 may be used for early diagnosis of colon cancer. The mechanism of the targeted binding of TCP-1 to colon cancer was also studied using immunoprecipitation, LC-MS and bioinformatics. After screening and identifying of the possible binding target proteins of TCP-1, keratin, type II cytoskeletal 5 was speculated to be the specific binding target protein of TCP-1 in human tumor tissue. Pharmacokinetics studies were conducted to investigate the target-mediated drug disposition of the new tumor-specific peptide by LC-MS/MS. The tissue distribution study showed that TCP-1 was found only in colon tumors (the target site) in tumor mice did not bind to any other tissues. Conjugating TCP-1 to tumor markedly increased its removal rate from blood circulation but mildly extended its staying time in vivo. In tumor mice, a lower AUC of TCP-1 (reduced by almost 35%) and 2-fold higher clearance were found compared to that of normal mice. The proposed metabolic pathway of TCP-1 in the kidney was also determined using LC-MSn-IT-TOF. The high specificity and low toxicity of the peptide may be caused by its extremely tight binding to the targets. Potential applications for future clinical use, including MRI and PET/CT were also explored, and this research may promote the development of colon cancer diagnostic technology research and provide new ideas and technical routes for tumor diagnostic technology.

Label-Free SERS Analysis of Serum Using Ag NPs/Cellulose Nanocrystal/Graphene Oxide Nanocomposite Film Substrate in Screening Colon Cancer.

Label-free surface-enhanced Raman scattering (SERS) analysis shows tremendous potential for the early diagnosis and screening of colon cancer, owing to the advantage of being noninvasive and sensitive. As a clinical diagnostic tool, however, the reproducibility of analytical methods is a priority. Herein, we successfully fabricated Ag NPs/cellulose nanocrystals/graphene oxide (Ag NPs/CNC/GO) nanocomposite film as a uniform SERS active substrate for label-free SERS analysis of clinical serum. The Ag NPs/CNC/GO suspensions by self-assembling GO into CNC solution through in-situ reduction method. Furthermore, we spin-coated the prepared suspensions on the bacterial cellulose membrane (BCM) to form Ag NPs/CNC/GO nanocomposite film. The nanofilm showed excellent sensitivity (LOD = 30 nM) and uniformity (RSD = 14.2%) for Nile Blue A detection. With a proof-of-concept demonstration for the label-free analysis of serum, the nanofilm combined with the principal component analysis-linear discriminant analysis (PCA-LDA) model can be effectively employed for colon cancer screening. The results showed that our model had an overall prediction accuracy of 84.1% for colon cancer (n = 28) and the normal (n = 28), and the specificity and sensitivity were 89.3% and 71.4%, respectively. This study indicated that label-free serum SERS analysis based on Ag NPs/CNC/GO nanocomposite film combined with machine learning holds promise for the early diagnosis of colon cancer.

The role of deep learning in diagnosing colorectal cancer.

Colon cancer is a major public health issue, affecting a growing number of individuals worldwide. Proper and early diagnosis of colon cancer is the necessary first step toward effective treatment and/or prevention of future disease relapse. Artificial intelligence and its subtypes, deep learning in particular, tend nowadays to have an expanding role in all fields of medicine, and diagnosing colon cancer is no exception. This report aims to summarize the entire application spectrum of deep learning in all diagnostic tests regarding colon cancer, from endoscopy and histologic examination to medical imaging and screening serologic tests.

The Importance of MicroRNA 29a as a Biomarker in Colon Cancer

Introduction: Colon cancer (CC) has become one of the most common diseases in recent years.The incidence of (CC) varies greatly worldwide, depending on lifestyle, environment, and genetic causes. Endoscopy is invasive and expensive for early detection. Therefore, there is a need to develop reliable and non-invasive markers for the early diagnosis of CC. MicroRNAs (miRNAs) have attracted attention as promising biomarkers in other cancers In this study, our aim is to determine whether miRNAs are biomarkers in the early diagnosis of colon cancer. Materials and Methods: Twenty patients diagnosed with colon cancer and twenty healthy individuals of the same age and gender were selected as the control group. Four miRNAs (let-7g, miR-29a, miR-155, miR-200c) selected and MiR 181 and miR 192 used as the endogenous control group in line with their binding potentials and gene expression levels. They were measured with StepOne TM Real-Time PCR. Results: mir29a was significantly high AUCs, thus the sensitivity and specificity values of was 100.0% and 64.3%. Common to all studies was MiR29a as sensitive and spesific like in our study. Conclusion: miR29a, is a hopeful miRNA in the early diagnosis of colon cancer, prognosis and treatment of the disease. Further validation studies are needed.

Mechanism of Glycitein in the Treatment of Colon Cancer Based on Network Pharmacology and Molecular Docking

Introduction: The prevalence of colon cancer remains high across the world. The early diagnosis of colon cancer is challenging. Moreover, patients with colon cancer frequently suffer from poor prognoses. Methods: Differentially expressed genes (DEGs) in colon cancer were acquired based on TCGA-COAD dataset screening. DEGs were input into the Connectivity Map (CMap) database to screen small molecule compounds with the potential to reverse colon cancer pathological function. Glycitein ranked first among the screened small-molecule compounds. We downloaded the main targets of glycitein from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) database and constructed protein-protein interaction (PPI) networks of those which were closely related to targets by the Search Tool for the Retrieval of Interaction Gene/Proteins (STRING). Five potential targets of glycitein for treating colon cancer were identified (CCNA2, ESR1, ESR2, MAPK14, and PTGS2). These targets were used as seeds for random walk with restart (RWR) analysis of PPI networks. Then, the interaction network of glycitein-colon cancer-related genes was constructed based on the top 50 genes in affinity coefficients. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted on the potential genes targeted by glycitein in colon cancer treatment and those that were closely bound up with targets. Results: GO analysis demonstrated that the enrichment of these genes was primarily discovered in biological functions including regulation of fibroblast proliferation, response to oxygen levels, and epithelial cell proliferation. The KEGG analysis results illustrated that the signaling pathways where these genes were mostly involved consisted of the mitogen-activated protein kinase signaling pathway, the phosphatidylinositol-3-kinase-Akt signaling pathway, and the p53 signaling pathway. Finally, stable binding of glycitein to five potential targets in colon cancer was verified by molecular docking. Conclusion: This study elucidated the key targets and main pathways of glycitein on the basis of network pharmacology and preliminarily analyzed molecular mechanisms in the treatment of colon cancer. A scientific basis is provided for glycitein application in treating colon cancer.

The expression and clinical significance of Hook1 in colorectal cancer

Purpose — Colorectal cancer is one of the most common gastrointestinal malignancies around the world. Early diagnosis of colon cancer is still a difficult problem for clinicians. This study aimed to determine the expression of Hook1 in colon cancer and evaluate its clinical role. Methods — Real-time Quantitative PCR was performed to characterize the difference of Hook1 mRNA expression between colon cancer specimens and normal colon specimens. Immunohistochemistry was used to evaluate the Hook1 expression in colon cancer tissue. Analyses of correlation between Hook1 level and clinicopathologic features and prognosis were also performed. Results — In total, 70 pair of surgical samples of patients with colorectal cancer were collected. And 70 patients were received postoperative following-up with the median follow-up being 38 months. Enhanced Hook1 mRNA levels were observed in colorectal cancer tissues (1.49%) compared to normal tissues (0.76%). IHC also revealed significantly higher rates of Hook1 expression amongst 58 cases in colon cancer tissues versus normal colon. Higher Hook1 protein levels were associated with better differentiation degree and easier likelihood of lymph node metastasis. Patients with relatively lower Hook1 level survived extended period without disease progression. And age, distant metastasis, TNM stage and Hook1 protein expression were marginally identified as poor prognosis indicator. Conclusion — Hook1 is highly expressed in colorectal cancer tissues, which correlate to well differentiated degree and lymph node metastasis tendency of colorectal cancer and potentially identified as a poor prognosis predictor.

Loss of CHGA Protein as a Potential Biomarker for Colon Cancer Diagnosis: A Study on Biomarker Discovery by Machine Learning and Confirmation by Immunohistochemistry in Colorectal Cancer Tissue Microarrays.

Simple SummaryThe identification of effective novel biomarkers is emergently needed in colon cancer patients. In the present study, firstly we predicted that CHGA could be a biomarker for colon cancer based on the protein–protein interaction network of all the reported biomarkers that were collected from our colorectal cancer biomarker database (CBD). Then we verified our results using a diagnostic test in gene expression data and an immunohistochemistry test. The results of this study suggest that a loss of CHGA expression from the normal colon and adjacent mucosa to colon cancer may be used as a valuable biomarker for early diagnosis of colon cancer patients.Background. The incidence of colorectal cancers has been constantly increasing. Although the mortality has slightly decreased, it is far from satisfaction. Precise early diagnosis for colorectal cancer has been a great challenge in order to improve patient survival. Patients and Methods. We started with searching for protein biomarkers based on our colorectal cancer biomarker database (CBD), finding differential expressed genes (GEGs) and non-DEGs from RNA sequencing (RNA-seq) data, and further predicted new biomarkers of protein–protein interaction (PPI) networks by machine learning (ML) methods. The best-selected biomarker was further verified by a receiver operating characteristic (ROC) test from microarray and RNA-seq data, biological network, and functional analysis, and immunohistochemistry in the tissue arrays from 198 specimens. Results. There were twelve proteins (MYO5A, CHGA, MAPK13, VDAC1, CCNA2, YWHAZ, CDK5, GNB3, CAMK2G, MAPK10, SDC2, and ADCY5) which were predicted by ML as colon cancer candidate diagnosis biomarkers. These predicted biomarkers showed close relationships with reported biomarkers of the PPI network and shared some pathways. An ROC test showed the CHGA protein with the best diagnostic accuracy (AUC = 0.9 in microarray data and 0.995 in RNA-seq data) among these candidate protein biomarkers. Furthermore, immunohistochemistry examination on our colon cancer tissue microarray samples further confirmed our bioinformatical prediction, indicating that CHGA may be used as a potential biomarker for early diagnosis of colon cancer patients. Conclusions. CHGA could be a potential candidate biomarker for diagnosing earlier colon cancer in the patients.

Screening Gene Expression-Related Alternative Splicing Event Signature for Colon Cancer Prognostic Prediction.

Colon cancer is a kind of common intestinal disease, and early diagnosis of colon cancer is crucial for patient's prognosis. RNA alternative splicing (AS) is an RNA modification that affects cancer occurrence. RNA AS detection is promising to improve the in-depth understanding of the pathological mechanisms in colon cancer. In this study, differential analysis was performed to determine colon cancer-related AS events and the corresponding parental genes. Subsequently, GO functional annotation analysis was carried out on the parental genes, which revealed that these AS events might affect cell adhesion and cell growth. Besides, protein-protein interaction (PPI) network was established with the parental genes, in which MCODE was utilized to identify major functional modules. Enrichment analysis for the major functional module was implemented again, which demonstrated that these genes were mainly concentrated in the ribosome, protein ubiquitination, cell adhesion molecule binding, and other relevant biological functions. Next, differentially expressed genes (DEGs) were screened from colon cancer and normal tissues and overlapped with the parental genes, by which 55 gene expression-associated AS and the corresponding 45 genes were obtained. Moreover, a correlation analysis between splicing factors (SFs) and AS was done to identify interactions. On this basis, an SF-AS network was constructed. The univariate Cox regression analysis was employed to screen prognostic AS signature and establish a risk model. To assess the model, K-M and ROC analyses were done for model assessment, indicating the effective prediction performance. Combined with common clinicopathological features, the multivariate Cox regression analysis was conducted to confirm whether the risk model could be considered as an independent prognostic indicator. Finally, the expression status of the parental genes for the prognostic AS was evaluated between normal and colon cancer cells using qRT-PCR. In summary, TCGA SpliceSeq data were comprehensively analyzed, and a 5-AS prognostic model was constructed for colon cancer.

The correlation between intestinal microbiota and colorectal cancer

The study of intestinal and oral microflora and their metabolites playing an important role in intestinal homeostasis, has led to the identification of species closely related to the development of colorectal cancer, intracellular correlations of fungi and bacteria compared to control. The correlation between oral microbiota and intestinal microflora, as well as associated with the mucous membrane of the large intestine, was revealed. It was noted that the use of eu- and probiotics improved the immunological indices and the structure of the intestinal microbiota. Thus, studying the oral and intestinal microbiota and its metabolites may prove to be a simple, accessible and informative method for the early diagnosis of colon cancer. However, most studies indicate only changes in the quantitative and qualitative composition of the microbiota, hardly revealing its cause-effect relations with the processes of tumor formation in the colon. Therefore, it is necessary to continue studies of this problem.

Colon Tissues Classification and Localization in Whole Slide Images Using Deep Learning

Colorectal cancer is one of the leading causes of cancer-related death worldwide. The early diagnosis of colon cancer not only reduces mortality but also reduces the burden related to the treatment strategies such as chemotherapy and/or radiotherapy. However, when the microscopic examination of the suspected colon tissue sample is carried out, it becomes a tedious and time-consuming job for the pathologists to find the abnormality in the tissue. In addition, there may be interobserver variability that might lead to conflict in the final diagnosis. As a result, there is a crucial need of developing an intelligent automated method that can learn from the patterns themselves and assist the pathologist in making a faster, accurate, and consistent decision for determining the normal and abnormal region in the colorectal tissues. Moreover, the intelligent method should be able to localize the abnormal region in the whole slide image (WSI), which will make it easier for the pathologists to focus on only the region of interest making the task of tissue examination faster and lesser time-consuming. As a result, artificial intelligence (AI)-based classification and localization models are proposed for determining and localizing the abnormal regions in WSI. The proposed models achieved F-score of 0.97, area under curve (AUC) 0.97 with pretrained Inception-v3 model, and F-score of 0.99 and AUC 0.99 with customized Inception-ResNet-v2 Type 5 (IR-v2 Type 5) model.

P-262 Staining plasma with lipophilic dye followed by size-exclusion chromatography, immune-capturing and on-bead flow cytometry is a highly sensitive approach to quantifying colorectal cancer derived extracellular nanovesicles

Development of new and effective methods for screening and early diagnosis of colon cancer is an important scientific and technological issue. There is a tendency to move from invasive methods to liquid biopsy. Extracellular nanovesicles (ENVs) secreted by colon cancer cells may serve as a marker of disease. Standard approaches to analyze cancer-derived ENVs in plasma suppose consequent steps of ENV isolation followed by immune-based labeling and quantification. However, at the early stage of cancer development, the amount of ENVs derived from cancer cells is supposedly not sufficient to be detected by available methods. Low sensitivity of currently available methods and low concentration of colon cancer-derived ENVs (cc-ENVs) compromise overall ENV- based approach for early cancer diagnosis. To solve this issue, we explored ‘inverse’ technology of cc-ENV quantification. This approach supposes three consequent steps: firstly, the labelling of all membranous components of plasma with lipophilic dye (CM-Dil); secondly, the isolation of population of ENVs formed by endosomal route (exosomes) by size-exclusion chromatography; thirdly, the quantification of tumor-specific fraction of these vesicles by flow cytometry with immune-beads. To prove the concept of this approach, we isolated ENVs from colon cancer cells (COLO 320) by ultracentrifugation according to the official rules of MISEV2018. EV concentrations were estimated by the NTA (NanoSight NS300). 20*1010, 10*1010 and 1*1010 EVs were added to the same amount ml of healthy donor blood plasma. The mixtures were colored and purified using the developed method. To isolate and quantify ccENVs, we first defined markers present in surface of ENVs secreted by COLO320 cells (GPA33 and RegIV) and created immune-bead decorated with antibodies specific to these markers. EVs were analyzed by flow cytometry (CytoFLEX) for the above markers with immune beads. GPA33(+) and RegIV (+) ENVs were isolated from mixture of CM-Dil-labeled ENVs and assayed by flow cytometry. The analysis of PE-positive events was carried out by FlowJo 10.7.1 software. The amount of GPA33(+) and RegIV (+) ENVs derived from colon cancer cells and estimated by developed method had obvious dose-dependency. The 20*1010, 10*1010 and 1*1010 cc-ENVs diluted in 2 mL of healthy donor’s plasma (which had a background signal without cc-ENVs) were quantified as 272, 177, 119 (CV, %) and 346, 179, 83 (CV, %), respectively. Since the portion of artificially mixed cc-ENVs could be estimated in a range of 0,1 – 1% of the total amount of plasma ENVs, proposed methods provide exceptional sensitivity for cc-ENV detection and might by applied for cancer screening. The proposed technique allowed for the quantification of minimal numbers of specific colon cancer-derived ENVs in plasma and can be applied in the frame of ENV-based methods of colon cancer diagnosis. The new technology may also be used to improve sensitivity of ENV-based diagnostic methods of other cancer types.

Rapid and accurate identification of colon cancer by Raman spectroscopy coupled with convolutional neural networks

Colonoscopy is regarded as the gold standard in colorectal tumor diagnosis, but it is costly and time-consuming. Raman spectroscopy has shown promise for differentiating cancerous from non-cancerous tissue and is expected to be a new tool for oncological diagnosis. However, traditional Raman spectroscopy analysis requires tedious preprocessing, and the classification accuracy needs to be improved. In this work, a novel Raman spectral qualitative classification method based on convolutional neural network (CNN) is proposed for the identification of three different colon tissue samples, including adenomatous polyp, adenocarcinoma and normal tissues. Experimental results show that this CNN model has superior feature extraction ability. For the spectral data of new individuals, the trained CNN model presents much better classification performance than traditional machine learning methods, such as the k-nearest neighbor, random forest, and support vector machine. Raman spectroscopy combined with CNN can be used as an effective auxiliary tool for the early diagnosis of colon cancer.

Comprehensive Analysis of Cyclin Family Gene Expression in Colon Cancer.

Colon cancer is a common malignancy of the digestive tract with high morbidity and mortality. There is an urgent need to identify effective biomarkers for the early diagnosis of colon cancer and to prolong patient survival. Cyclins are a family of proteins that directly participate in the cell cycle and are associated with many types of tumors, but the role and regulatory mechanism of most cyclin family members in colon cancer remain unclear. Here, we provide a systematic and comprehensive study of cyclin family gene expression and their potential roles in colon cancer. Pan-cancer analysis revealed that cyclin genes were most differentially expressed in colon adenocarcinoma. Among the four datasets of colon cancer from The Cancer Genome Atlas and the Gene Expression Omnibus, six cyclin genes (CCNA2, CCNB1, CCND1, CCNE1, CCNF, and CCNJL) were differentially expressed between normal and tumor tissues. Four of them (CCNA2, CCNB1, CCNE1, and CCNF) were notably elevated in the early TNM stages and significantly correlated with overall survival. Meanwhile, the expression of CCNA2 and CCNB1 was positively correlated with tumor-killing immune cells, such as CD8+ T cells.The copy numbers of CCNA2, CCNB1, CCND1, CCNE1, and CCNF was positively related to gene expression. The methylation levels of CCNB1 were lower in tumor tissues than in normal tissues and were negatively correlated with gene expression. The receiver operating characteristic curves indicated that the gene expression of 24 cyclins had higher predictive accuracy than the TNM stage. Pathway analysis showed that cyclin genes were tightly associated with apoptosis, the cell cycle, hormone ER, the RAS/MAPK pathway, mismatch repair, mTORC1 signaling, KRAS signaling, Akt, and TGFB in colon cancer. Weighted gene co-expression network analysis suggested that cyclin genes were closely linked to CDK1, BIRC5, PLK1, and BCL2L12. At the protein level, Cyclin A2 and Cyclin B1 were also expressed higher in colon adenocarcinoma tissues. In addition, cyclin genes were highly related to the drug sensitivity of some FDA-approved drugs, such as MEK and EGFR inhibitors, which might provide guidance for clinical treatment. In conclusion, cyclin genes are promising biomarkers for the diagnosis and prognosis of colon cancer.

Characterization of the parietal intestinal microbiota in colon cancer

Introduction. Colon cancer (RCC) clinically manifests itself in the late stages, therefore, early diagnosis presents significant difficulties. There are many types of microorganisms in the human colon that form symbiosis with intestinal cells to maintain normal function. Аim of the study was to determine the composition of the parietal microflora of the colon under conditions of a tumor process and to assess the possibility of using these data in the diagnosis of colon cancer. Material and methods. The main group included 63 patients operated on for cancer (adenocarcinoma) of the left half of the colon (descending, sigmoid, rectosigmoid regions) with I T1-2N0M0, II T3-4aN0M0, III T1-2N1M0 stages of the tumor process. Among them are 32 men and 31 women aged 20 to 75 years (57.7 ± 3.8 years). In all patients, before hospitalization, the tumor was confirmed by colonoscopy followed by histological examination. The group of clinical comparison in the amount of 25 people consisted of patients with chronic hemorrhoids without exacerbation, who underwent colonoscopy. Material from the main group, biopsies of tumor tissue and visually unchanged colon mucosa, were taken intraoperatively during tumor removal. In patients of the clinical comparison group, the material was taken during the colonoscopy process. The composition of the intestinal microbiota was determined by a bacteriological method. Results and discussion. As a result of comparing the colon microbiota of cancer patients and the clinical comparison group, statistically significant differences in the quantitative composition of Lactobacillus spp., Bifidobacterium spp., Bacteroides spp., Clostridium spp., Enterococcus spp., Escherichia coli (typical), Escherichia coli (lactose-negative), Enterobacteriaceae, Staphylococcus spp. (CNS), Candida spp. It was established for the first time that Bifidobacterium spp., Enterococcus spp. and the age of the patient can be further used in the diagnosis of a malignant process. Conclusion. The created additive model can be used as an additional screening criterion in the early diagnosis of colon cancer.

Construction of competing endogenous RNA network and identification of novel molecular biomarkers in colon cancer: A bioinformatic analysis.

Colon cancer patients suffer from high incidence and mortality rates worldwide. More novel molecular biomarkers should be used for the diagnosis and treatment of colon cancer. Long noncoding RNAs (lncRNAs) are found to be involved in colon cancer tumorigenesis and metastasis. This study aimed to identify novel lncRNAs in colon cancer.Two independent datasets (GSE70880 and GSE110715) were downloaded from the Gene Expression Omnibus database and merged with the sva package. R software was used to distinguish differentially expressed lncRNAs and mRNAs in the merged dataset. The competing endogenous RNA (ceRNA) network was constructed using differentially expressed lncRNAs and mRNAs with Cytoscape. Differentially expressed RNAs in the ceRNA network were further verified using the Cancer Genome Atlas database. Gene oncology analysis, Kyoto Encyclopedia of Genes and Genomes pathway enrichment and survival analysis were also performed to identify hub genes.A total of 99 differentially expressed lncRNAs and 95 differentially expressed mRNAs were identified in the merged database. Ten lncRNAs, 8 miRNAs, and 6 mRNAs were involved in the ceRNA network, in which LINC00114 and UCA1 were highly expressed in colon cancer. They were both associated with early tumor stages and might be used for the early diagnosis of colon cancer. High expression of LINC00114 can lead to poor overall survival of colon cancer patients. Furthermore, new pathways such as LINC00114/miR-107/PCKS5, UCA1/miR-107/PCKS5, and UCA1/miR-129-5p/SEMA6A were identified.Two novel lncRNAs (LINC00114 and UCA1) in colon cancer were identified by bioinformatics analysis. They might contribute to the occurrence and development of colon cancer. In addition, LINC00114 may be involved in the overall survival of colon cancer patients.

Detection of effective genes in colon cancer: A machine learning approach

Nowadays, a variety of cancers have become common among humans which unfortunately are the cause of death for many of these people. Early detection and diagnosis of cancers can have a significant impact on the survival of patients and treatment cost reduction. Colon cancer is the third and the second main cause of women's and men's death worldwide among cancers. Hence, many researchers have been trying to provide new methods for early diagnosis of colon cancer. In this study, we apply statistical hypothesis tests such as t -test and Mann–Whitney–Wilcoxon and machine learning methods such as Neural Network, KNN and Decision Tree to detect the most effective genes in the vital status of colon cancer patients. We normalize the dataset using a new two-step method. In the first step, the genes within each sample (patient) are normalized to have zero mean and unit variance. In the second step, normalization is done for each gene across the whole dataset. Analyzing the results shows that this normalization method is more efficient than the others and improves the overall performance of the research. Afterwards, we apply unsupervised learning methods to find the meaningful structures in colon cancer gene expressions. In this regard, the dimensionality of the dataset is reduced by employing Principle Component Analysis (PCA). Next, we cluster the patients according to the PCA extracted features. We then check the labeling results of unsupervised learning methods using different supervised learning algorithms. Finally, we determine genes which have major impact on colon cancer mortality rate in each cluster. Our conducted study is the first which suggests that the colon cancer patients can be categorized into two clusters. In each cluster, 20 effective genes were extracted which can be important for early diagnosis of colon cancer. Many of these genes have been identified for the first time.

Expression and diagnostic value of miR-34c and miR-141 in serum of patients with colon cancer.

Expression of miR-34c and miR-141 in serum of colon cancer patients and their association with clinicopathological features and diagnostic value for colon cancer were investigated. A total of 64 patients with colon cancer admitted to Hubei Cancer Hospital from January 2016 to March 2018 were included in the experimental group, and 64 healthy subjects undergoing physical examination during the same period were the control group. The expression of miR-34c and miR-141 in serum of patients in the two groups were detected by RT-qPCR, and the association of miR-34c and miR-141 with the clinicopathological characteristics of colon cancer patients was analyzed. The receiver operating characteristic (ROC) curve was used to assess the diagnostic efficiency of miR-34c and miR-141 in colon cancer. The expression of miR-141 in serum of patients in the experimental group was significantly higher than that in the control group (P<0.05). Expression of miR-34c in serum of patients in the experimental group was significantly lower than that in the control group (P<0.05) and the expression of miR-34c and miR-141 in serum of the experimental group were associated with tumor diameter, clinical stage, degree of differentiation and lymph node metastasis (P<0.05). AUC of serum miR-34c in the diagnosis of colon cancer was 0.857 (95% CI: 0.795-0.919), with the cut-off value of 0.800, the diagnostic sensitivity of 84.38%, and the specificity of 68.75% and AUC of serum miR-141 in the diagnosis of colon cancer was 0.876 (95% CI: 0.810-0.941), with the cut-off value of 0.282, the diagnostic sensitivity of 70.31%, and the specificity of 96.88%. The ROC curve for the diagnosis of colon cancer was further plotted in combination with serum miR-34c and miR-141. AUC of the two combined for the diagnosis of colon cancer was 0.929 (95% CI: 0.884-0.974), with the cut-off value of 0.566, the diagnostic sensitivity of 84.38%, and the specificity of 93.75%. In conclusion, miR-34c and miR-141 might be involved in the occurrence and progression of colon cancer and could be used as biological indicators for early diagnosis of colon cancer.