Diagnosis Of Colon Cancer Research Articles

Upper and Lower Endoscopic Findings in Mesenteric Panniculitis Patients: A Case-Control Study

Background: The natural history and prognosis of mesenteric panniculitis (MP) are not well-described. Despite referral for colonoscopy being common for this indication, colonoscopy findings in MP patients have not been reported. Therefore, we aimed to describe upper and lower gastrointestinal (GI) endoscopy findings in patients with mesenteric panniculitis, compared to matched controls, to investigate their clinical outcomes including incidence of malignancy and mortality. Methods: Retrospective case–control study was conducted, and included patients who were diagnosed with mesenteric panniculitis according to Coulier radiologic criteria on abdominal computerized tomography between 1/2005 and 12/2019, and followed to 12/2021. The case group was compared to a matched control group without MP on abdominal CT. Clinical data and the upper and lower endoscopies’ reports were reviewed in both groups. We excluded patients who, beyond diagnosis of MP, were also diagnosed with current malignancy, significant intra-abdominal morbidity or inflammatory bowel disease. Results: The initial set of 376 patients with MP, after exclusion, included 187 patients. A total of 56.1% were male, with a mean age 60 ± 15 years. Of them, 74 (39%) patients underwent follow-up CT scans, which demonstrated, in 66 (89.2%) patients, a stable MP without any aggravation. Colonoscopy was performed in 89 MP patients, and 98/187 controls. No significant difference in the colonoscopies’ findings was found between the two groups. Gastroscopy was performed in 84 MP and 79 controls. No case of gastric cancer was found. No statistically significant difference was found in the rate of gastroscopy findings. By the end of the follow-up period, malignancy was diagnosed in four patients of the MP group. None were colon cancer. The mortality rate in the MP group was 3.2%, without a significant difference compared to the controls. None were MP related. Conclusions: MP identified on abdominal CT is not associated with pathologic endoscopy findings or future diagnosis of colon cancer, and also has no impact on mortality rate. Since repeating abdominal CT did not reveal any disease progression, the necessity of follow-up imaging for MP should be carefully reconsidered.

Increased MAGE-C Family Gene Expression Levels as a Biomarker of Colon Cancer Through the Demethylation Mechanism

Background/Objectives: Colon cancer (CC) in Saudi Arabia is associated with a high death rate and is commonly identified at a more progressive stage. Therefore, it is critical to identify and characterize potential novel cancer-specific biomarkers to enhance early CC diagnosis. The goal was to assess their potential use as cancer biomarkers for the early detection and improvement of CC treatment. Methods: MAGE-C1, MAGE-C2, and MAGE-C3 family gene expression levels were examined using RT-PCR and qRT-PCR assays in 26 adjacent normal colon (NC) and CC tissue samples from male and female Saudi patients. Using several cell lines and the qRT-PCR technique, epigenetic control was also investigated to determine whether reduced treatment with 5-aza-2′-deoxycytidine, which reduces DNA methyltransferase, can increase the expression of the MAGE-C gene. The expression levels, promoter methylation, and prognostic significance of MAGE-C1, MAGE-C2, and MAGE-C3 genes across various cancers were analyzed using The Cancer Genome Atlas (TCGA) data. Additionally, the prognostic significance of these genes was assessed through Kaplan–Meier survival analysis. Results: The RT-PCR results showed that MAGE-C1, MAGE-C2, and MAGE-C3 gene expressions were significantly higher in the CC and NC tissues. The MAGE-C1 expression level was the highest in CC tissues (p < 0.0001), followed by MAGE-C3 (p = 0.0004) and MAGE-C2 (p = 0.0020) in descending order. The 5-aza-2′-deoxycytidine treatment significantly increased the mRNA expression levels of the MAGE-C1, MAGE-C2, and MAGE-C3 genes in HCT116, Caco-2, MCF-7, and MCF-10A cells. Expression analyses of TCGA samples revealed significant upregulation of these genes in several cancer types, with notable differences between normal, tumor, and metastatic tissues. Promoter methylation indicates hypomethylation in cancerous tissues. Survival analyses show that high expression levels of MAGE-C1 correlate with better prognosis, while MAGE-C3 is associated with poorer outcomes. Conclusions: These results demonstrate that MAGE-C genes are viable prospective biomarkers of CC controlled by hypomethylating drugs, consequently offering a possible treatment target for CC in a specific population.

FFS-Net: Fourier-based segmentation of colon cancer glands using frequency and spatial edge interaction

The morphological features of glands provide a reliable basis for pathologists to diagnose colon cancer correctly. Currently, most methods are limited in their ability to address blurred edges, adhesions, and morphological differences in glands when working in the spatial domain. In this study, a new perspective is proposed to segment glands by utilizing the interaction between Fourier and spatial domain features. Specifically, a Fourier transform is used to analyze the frequency characteristics of pathological images in which the phase component is related to image structural information and the amplitude component contains image intensity information. A Fourier-based colon cancer gland segmentation network (FFS-Net) is proposed, which gradually constructs feature representations in both the spatial and frequency domains through spatial frequency edge interactions. This network comprises spatial subnetworks, frequency subnetworks, and an edge phase fusion module (EPFM). To alleviate the interference of complex background information in feature discrimination, the spatial subnetwork collects high-frequency contour information regarding the glands. Then, the EPFM completes the spatial-frequency feature interaction to add edge-related constraints to the extracted features, enhance edge representation, and obtain global features from the frequency domain, thereby, addressing significant differences in gland morphology. Finally, learnable Gabor filters are introduced to enhance edge texture detail information in the frequency subnetwork, which further improves the segmentation of glands exhibiting blurred edges. The experimental results demonstrate that FFS-Net’s performance on two representative datasets was competitive and highly interpretable.

Colorectal cancer

Colon and rectal cancers affect the large intestine and rectum, the lowest parts of the digestive system. This article covers key facts on the signs, symptoms, diagnosis, and treatment of early-stage colon and rectal cancer.

Association Between the Protein Expressions of MutS Homologs and Villin and the Clinicopathological Characteristics in 310 Colon Cancer Patients

To examine the relationship between the expressions of mismatch repair proteins, MutS homolog 2 (MSH2) and MutS homolog 6 (MSH6), and villin and the pathological features in patients with colon cancer. A total of 310 cases of colon cancer patients who were treated at our hospital between January 2017 and September 2021 were selected. The diagnosis of colon cancer of all patients was verified by pathological evaluation. Immunohistochemistry was used to determine the protein expressions of MSH2, MSH6, and villin. The correlation between the expressions of MSH2, MSH6, and villin and the clinicopathological parameters in patients with colon cancer was analyzed accordingly. Multivariate logistic regression was used to analyze the correlation between the expressions of MSH2, MSH6, and villin and the clinicopathological parameters of colon cancer. Kaplan-Meier survival curve was used to compare the 2-year survival rates of colon cancer patients with different expression levels of the proteins. Among the 310 patients with colon cancer, the negative expression rates of MSH2, MSH6, and villin proteins in cancer tissues were 8.71% (27/310), 9.35% (29/310), and 46.13% (143/310), respectively. The negative expression rates of the three proteins in tissues adjacent to cancer were 3.23% (10/310), 4.19% (13/310), and 9.68% (30/310), respectively. The negative expression rates of the three proteins in cancer tissues were all higher than those in adjacent tissues (P<0.05). Regression analysis showed that the expression of MSH2 and MSH6 in cancer tissues was correlated with the age, the location of tumor lesions, tumor differentiation degree, and lymph node metastasis in colon cancer patients (P<0.05). The expression of villin in the cancer tissue is correlated with the depth of tumor infiltration, lymph node metastasis, distant metastasis, and clinical staging status in colon cancer patients (P<0.05). The 2-year survival rates of patients with negative expressions of MSH2 and MSH6 were 51.85% and 44.83%, respectively, which were lower than those of patients with positive expression of MSH2 and MSH6 (79.51% and 80.43%, P<0.05). Thirteen patients (4.1%) had negative expression of MSH2, MSH6, and villin (referred to as "triple negative expressions") in the cancer tissues, and their 2-year survival rate was 30.77%, which was lower than that of colon cancer patients who did not meet the criteria for triple negative expressions (79.12% [235/297], P<0.05). The expressions of MSH2, MSH6, and villin are closely correlated with the pathological features of colon cancer patients. Evaluating the expression of the three proteins may assist in the clinical diagnosis, treatment, and prognosis evaluation of colon cancer.

Biomedical image classification using seagull optimization with deep learning for colon and lung cancer diagnosis

&lt;p&gt;Traditional health care relies on biomedical image categorization to identify and treat various medical conditions. In machine learning and medical imaging, biomedical image classification for colon and lung cancer diagnosis is significant. The work focuses on building novel models and algorithms to accurately detect and categorize tumorous lesions using computer tomography (CT) scans and histopathology slides. These systems use image processing, deep learning (DL), and convolutional neural networks (CNN) to assist medical professionals diagnose cancer sooner and improve patient outcomes. Biomedical image classification using seagull optimization with deep learning (BIC-SGODL) addresses colon and lung cancer diagnosis. The BIC-SGODL method improves cancer diagnosis using hyperparameter optimized DL model. BIC-SGODL utilizes DenseNet to learn complicated features. The convolutional long short-term memory (CLSTM) standard captures spatiotemporal information in sequential picture data. Finally, the SGO method adjusts hyperparameters to improve model performance and generalization. BIC-SGODL performs well with biomedical image dataset simulations. Thus, medical picture cancer diagnosis may be automated using BIC-SGODL.&lt;/p&gt;

Exploration of the ubiquitination-related molecular classification and signature to predict the survival and immune microenvironment in colon cancer.

Ubiquitination, a major post-translational modification, significantly impacts tumorigenesis, progression, and prognosis. This study aims to classify colon cancer at the molecular level and create a reliable signature using ubiquitination-related genes (URGs) to assess the immune microenvironment and prognosis. We employed non-negative matrix factorization to subtype colon cancer based on ubiquitination-related gene (URG) expression patterns. Quantitative scores for 28 immune cell infiltrates and the tumor microenvironment were computed using single-sample gene set enrichment analysis (ssGSEA) and the Estimate algorithm. Subtype feature genes were selected through Lasso logistic regression and SVM-RFE algorithm. The ubiquitination-related signature was constructed using univariate Cox, Lasso, and stepwise regression methods to categorize patients into high and low-risk groups. Validation included log-rank tests, receiver operating characteristic (ROC) analysis, decision curve analysis (DCA), and external dataset validation. Immune therapy response was compared using Tumor Immune Dysfunction and Exclusion (TIDE), Immunophenoscore (IPS), and submap analyses. Clinical variables and risk scores were integrated into an enhanced nomogram. The early diagnostic value of four URGs was confirmed via quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry. The cell proliferation was assessed through colony formation, EdU staining, and xenograft tumorigenesis assays. Prognostic ubiquitination-related genes (URGs) stratified patients into subtypes, revealing differences in survival, immune cell infiltration, and pathological staging. A signature of 6 URGs (ARHGAP4, MID2, SIAH2, TRIM45, UBE2D2, WDR72) was identified from 57 subtype-related genes. The high-risk group exhibited characteristics indicative of enhanced epithelial-mesenchymal transition, immune escape, immunosuppressive myeloid-derived suppressor cells, regulatory T cell infiltration, and lower immunogenicity. In contrast, the low-risk group demonstrated the opposite trend but showed a better response to CTLA4 checkpoint inhibitors. The predictive performance of the nomogram significantly improved with the integration of risk score, stage, and age. ARHGAP4 and SIAH2 exhibit promising early diagnostic capabilities. Additionally, WDR72 knockdown significantly inhibited CRC cell proliferation both in vitro and in vivo. Our developed ubiquitination-related signature and genes serve as promising biomarkers for colon cancer prognosis, immune microenvironment, and diagnosis.

Global transcriptomic network analysis of the crosstalk between microbiota and cancer-related cells in the oral-gut-lung axis.

The diagnosis and treatment of lung, colon, and gastric cancer through the histologic characteristics and genomic biomarkers have not had a strong impact on the mortality rates of the top three global causes of death by cancer. Twenty-five transcriptomic analyses (10 lung cancer, 10 gastric cancer, and 5 colon cancer datasets) followed our own bioinformatic pipeline based on the utilization of specialized libraries from the R language and DAVID´s gene enrichment analyses to identify a regulatory metafirm network of transcription factors and target genes common in every type of cancer, with experimental evidence that supports its relationship with the unlocking of cell phenotypic plasticity for the acquisition of the hallmarks of cancer during the tumoral process. The network's regulatory functional and signaling pathways might depend on the constant crosstalk with the microbiome network established in the oral-gut-lung axis. The global transcriptomic network analysis highlighted the impact of transcription factors (SOX4, TCF3, TEAD4, ETV4, and FOXM1) that might be related to stem cell programming and cancer progression through the regulation of the expression of genes, such as cancer-cell membrane receptors, that interact with several microorganisms, including human T-cell leukemia virus 1 (HTLV-1), the human papilloma virus (HPV), the Epstein-Barr virus (EBV), and SARS-CoV-2. These interactions can trigger the MAPK, non-canonical WNT, and IFN signaling pathways, which regulate key transcription factor overexpression during the establishment and progression of lung, colon, and gastric cancer, respectively, along with the formation of the microbiome network. The global transcriptomic network analysis highlights the important interaction between key transcription factors in lung, colon, and gastric cancer, which regulates the expression of cancer-cell membrane receptors for the interaction with the microbiome network during the tumorigenic process.

Breaking Barriers in Cancer Diagnosis: Super‐Light Compact Convolution Transformer for Colon and Lung Cancer Detection

ABSTRACTAccording to the World Health Organization, lung and colon cancers are known for their high mortality rates which necessitate the diagnosis of these cancers at an early stage. However, the limited availability of data such as histopathology images used for diagnosis of these cancers, poses a significant challenge while developing computer‐aided detection system. This makes it necessary to keep a check on the number of parameters in the artificial intelligence (AI) model used for the detection of these cancers considering the limited availability of the data. In this work, a customised compact and efficient convolution transformer architecture, termed, C3‐Transformer has been proposed for the diagnosis of colon and lung cancers using histopathological images. The proposed C3‐Transformer relies on convolutional tokenisation and sequence pooling approach to keep a check on the number of parameters and to combine the advantage of convolution neural network with the advantages of transformer model. The novelty of the proposed method lies in efficient classification of colon and lung cancers using the proposed C3‐Transformer architecture. The performance of the proposed method has been evaluated on the ‘LC25000’ dataset. Experimental results shows that the proposed method has been able to achieve average classification accuracy, precision and recall value of 99.30%, 0.9941 and 0.9950, in classifying the five different classes of colon and lung cancer with only 0.0316 million parameters. Thus, the present computer‐aided detection system developed using proposed C3‐Transformer can efficiently detect the colon and lung cancers using histopathology images with high detection accuracy.

Racial differences of secondary metastasis in patients with colon cancer: A United States population-based cohort study.

80 Background: Disparities in health outcomes exist among racial and ethnic groups in the United States, notably in patients with colon cancer. Metastasis is the primary cause of cancer morbidity and mortality. However, data on the metastasis risks and clinical outcomes on hospitalized individuals with colon cancer is still limited. Therefore, we aim to assess the association between metastatic colon cancer and racial differences. Methods: We analyzed the 2020 U.S. National Inpatient Sample (NIS) to explore patients who have colon cancer as the primary diagnosis. Additionally, we identified evidence of metastasis, as recorded by ICD-10-CM. Adjusted odds ratios (aORs) for specified outcomes were calculated through multivariable logistic and linear regression analyses. The primary outcome was racial differences in organ metastasis and secondary outcomes included mortality and length of stay. Statistical significance was established at p-value of 0.05. Results: We identified 80,130 patients with primary diagnosis of colon cancer at discharge. The mean age was 67.4 years; 49.7% were female. Caucasians accounted for 69.3%, with African Americans at 13.3% and Hispanics at 8.2%. 15% of the patients had liver metastasis, whereas 22% had lung metastasis. In a multivariate analysis adjusting for patient, COVID-19, chemotherapy usage and hospital factors, African Americans had higher risk of lung metastasis (aOR 1.22; 95%CI (1.07, 1.40), p = 0.003), higher risk of liver metastasis (aOR 1.51; 95%CI (1.31, 1.74), p = 0.021) and longer length of stay (b = 0.95; 95%CI (0.56, 1.33), p = 0.001). Hispanics also had higher risk of lung (aOR 1.22, p &lt; 0.05) and liver metastasis (aOR 1.24, p = 0.021). We observed an increase in risk of metastasis and mortality but non-statistically significant in some parameters and races, as shown in table provided. Conclusions: In conclusion, our study revealed that racial difference is associated with higher risk of metastasis, as well as other outcomes. To establish a causal relationship between races, metastasis, and mortality in patients with colon cancer, further longitudinal research is necessary. [Table: see text]

Development of an Impedance and QCM-Based Electrochemical Biosensor for the Detection of Colon Cancer-Secreted Protein-2 (CCSP-2), a Potential Colorectal Cancer Biomarker

Research on colon cancer diagnosis is advancing due to the application of electrochemical biosensors. Early screening can significantly reduce the overall mortality rate from colorectal cancer (CRC). Colon cancer-secreted protein-2 (CCSP-2) is specifically overexpressed and secreted by cancerous cells and adenomas found in the colon. Utilizing a cost-effective and non-invasive electrochemical immunosensor for CCSP-2 detection can aid in the early diagnosis of disease recurrences in individuals with colon cancer. In this work, we designed a simplified electrochemical immunosensor consisting of a functionalized gold (Au) electrode using cysteine-modified recombinant protein G (RPGCys) to immobilize the CCSP-2 antibody. Electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV) have been used to analyze the electrochemical response caused by the binding between the target CCSP-2 and anti-CCSP-2, and there are significant changes observed for charge transfer resistance (Rct) in EIS and current density in CV. The quantification of mass changes attributed to binding events within the sensor will be determined through the application of quartz crystal microbalance (QCM). Angle-resolved X-ray photoelectron spectroscopy (ARXPS) and atomic force microscopy (AFM) will be used for the characterization of the sensor surface. The sensor's sensitivity and specificity will be assessed through the determination of the limit of detection and comprehensive testing of the probe with varied proteins and cell lines sourced from both cancerous and non-cancerous patient samples. This electrochemical probe is expected to enable the rapid and direct detection of early-stage colorectal cancer.

Identifying polyamine related biomarkers in diagnosis and treatment of ulcerative colitis by integrating bulk and single-cell sequencing data

Ulcerative colitis (UC) is a chronic inflammatory disorder of the colon, and its pathogenesis remains unclear. Polyamine metabolic enzymes play a crucial role in UC. In this study, we aimed to identify pivotal polyamine-related genes (PRGs) and explore the underlying mechanism between PRGs and the disease status and therapeutic response of UC. We analyzed mRNA-sequencing data and clinical information of UC patients from the GEO database and identified NNMT, PTGS2, TRIM22, TGM2, and PPARG as key PRGs associated with active UC using differential expression analysis and weighted gene co-expression network analysis (WCGNA). Receiver operator characteristic curve (ROC) analysis confirmed the accuracy of these key genes in UC and colitis-associated colon cancer (CAC) diagnosis, and we validated their relationship with therapeutic response in external verification sets. Additionally, single-cell analysis revealed that the key PRGs were specific to certain immune cell types, emphasizing the vital role of intestinal tissue stem cells in active UC. The results were validated in vitro and in vivo experiments, including the colitis mice model and CAC mice model. In conclusion, these key PRGs effectively predict the progression of UC patients and could serve as new pharmacological biomarkers for the therapeutic response of UC.

Comparative Study of Deep Learning Architectures for Accurate Colon Cancer Diagnosis

Comparative Study of Deep Learning Architectures for Accurate Colon Cancer Diagnosis

The value of FPR, FAR, CAR, CPR in the auxiliary diagnosis of colon cancer.

Chronic malnutrition, abnormal blood clotting, and systemic inflammation contribute to the occurrence and progression of colon cancer. This study aimed to assess the diagnostic utility of the 100fibrinogen-to-prealbumin ratio (FPR), 100fibrinogen-to-albumin ratio (FAR), 100C-reactive protein-to-albumin ratio (CAR), and 100C-reactive protein-to-prealbumin ratio (CPR) in aiding the diagnosis of colon cancer. A total of 129 patients with colon cancer were enrolled between April 2015 and August 2022. While 129 patients with colon adenoma were selected as the control group. The serum levels of FAR, FPR, CAR, CPR, CEA, and CA125 in the colon cancer group were significantly higher than those in the colon adenoma group (P < .05). In Logistic regression analysis, high FAR and high FPR were identified as independent risk factors for colon cancer. Receiver operating characteristic (ROC) curve analysis results showed that Among the combined measures, FAR, FPR, CAR, and CPR had the highest diagnostic efficacy in distinguishing colon cancer from colon adenomas (AUC = 0.886, Sen = 80.62%, Spe = 81.40%). Thus, FAR, FPR, CAR, and CPR may serve as valuable biomarkers for the diagnosis of colon cancer, and the combined detection of FAR, FPR, CAR, and CPR can enhance the diagnostic efficiency for both colon cancer and colon adenoma.

Income differences in time to colon cancer diagnosis.

People with low income have worse outcomes throughout the cancer care continuum; however, little is known about income and the diagnostic interval. We described diagnostic pathways by neighborhood income and investigated the association between income and the diagnostic interval. This was a retrospective cohort study of colon cancer patients diagnosed 2007-2019 in Ontario using routinely collected data. The diagnostic interval was defined as the number of days from the first colon cancer encounter to diagnosis. Asymptomatic pathways were defined as first encounter with a colonoscopy or guaiac fecal occult blood test not occurring in the emergency department and were examined separately from symptomatic pathways. Quantile regression was used to determine the association between neighborhood income quintile and the conditional 50th and 90th percentile diagnostic interval controlling for age, sex, rural residence, and year of diagnosis. A total of 64,303 colon cancer patients were included. Patients residing in the lowest income neighborhoods were more likely to be diagnosed through symptomatic pathways and in the emergency department. Living in low-income neighborhoods was associated with longer 50th and 90th-percentile symptomatic diagnostic intervals compared to patients living in the highest income neighborhoods. For example, the 90th percentile diagnostic interval was 15 days (95% CI 6-23) longer in patients living in the lowest income neighborhoods compared to the highest. These findings reveal income inequities during the diagnostic phase of colon cancer. Future work should determine pathways to reducing inequalities along the diagnostic interval and evaluate screening and diagnostic assessment programs from an equity perspective.

Rethinking encoder-decoder architecture using vision transformer for colorectal polyp and surgical instruments segmentation

Accurate polyp segmentation from colonoscopy images is important for the immediate diagnosis and effective treatment of colon cancer. While significant progress has been made in the polyps segmentation task, there are various challenges that need to be addressed. Polyps can vary greatly in size and shape, and often has no clear boundary between surrounding tissues. Furthermore, surgical instrument segmentation can aid surgeons with the precise positioning and orientation of the instruments, helping them to plan the next steps in the robot-assisted surgery. The proposed colorectal polyp segmentation Transformer (CPS-Former) uses innovative attention blocks in a network that encodes-decodes features like classic Semantic Segmentation Network (SegNet). However, it has special self-attention modules with small convolutional kernels that efficiently extract information from different feature-channels. Moreover, it is equipped with an effective positional embedding to capture information from a large area of context for long distance interactions. Additionally, a fusion block is embedded for scaling-attention that combines the outputs from the encoder-decoder blocks to enhance the semantic features and reduce the non-semantic ones. Transformer encoder blocks also modified by adding a local feedforward layer and skips connections, and adjust the channel sizes to reduce the model trainable parameters. We evaluate our colorectal polyp segmentation network (CPS-Former) on four colorectal polyp public datasets and one surgical instrument segmentation dataset, which show its superiority over other state-of-the-art polyp segmentation models. Our implementation source code and network weights are available at GitHub: https://github.com/ahmedeqbal/CPS-Former.

Epiplakin expression dynamics during colon carcinogenesis: Correlation with proliferation.

Colorectal cancer poses a significant global health challenge, with a considerable proportion arising from colon adenomas. Understanding the molecules involved in the carcinogenesis process is crucial for improving colon cancer diagnosis and prognosis. While research on the role of epiplakin in cancer remains limited compared to other plakin group proteins, comprehending its expression patterns and correlations can offer valuable insights into colon carcinogenesis. In this study, we analyzed 60 tissue samples, including colon adenocarcinomas, tubular adenomas (low malignancy risk group), tubulovillous adenomas (high malignancy risk group), and adjacent normal colon tissues. Classification and grading were reevaluated by histological examination. Immunohistochemistry was performed to assess epiplakin and Ki67 expression. Epiplakin optical density and the Ki67 proliferation index were calculated using ImageJ. Statistical analyses were conducted to evaluate correlations and significance. Epiplakin expression was significantly decreased in colon adenocarcinomas [optical density median 4.04 (95% CI, 3.98 to 4.24)] and tubulovillous adenomas [4.32 (95% CI, 4.08 to 4.32)] compared to normal colon tissues [4.61 (95% CI, 4.50 to 4.67)] and tubular adenomas [4.87 (95% CI, 4.67 to 4.88)] (P < 0.05). Moreover, adenoma groups exhibited higher proliferation indices (P < 0.05), and a positive correlation was found between epiplakin expression and the Ki67 proliferation index (r = 0.317, P < 0.05). Our study highlights the potential significance of epiplakin in colorectal cancer. Decreased epiplakin expression is associated with colon malignancy progression, suggesting its role as a potential marker.

Integrated microfluidic chip with a MIP-gated organic electrochemical transistor for continuous, selective, and label-free quantification of tumor marker

Early diagnosis of cancer is crucial to effective treatment of the patients and to higher survival rates. Tumor marker detection as an effective and noninvasive cancer diagnosis method has important clinical significance. N1, N12-diacetylspermine (DAS) is a new tumor marker. The development of its detection methods is an urgent need for medical diagnosis and human health monitoring. The conventional detection methods of DAS are labor-intensive and time-consuming approaches. In this work, a novel electrochemical detection platform was established by integrating an organic electrochemical transistor (OECT) with a microfluidic chip and applied for trace measurement of DAS. The sensing unit is a gate electrode in the OECT modified with molecularly imprinted polymers (MIPs), which can be used as an artificial receptor to realize label-free detection. The high specific recognition ability of MIPs films combined with the amplified function of an OECT yielded a highly sensitive and selective electrochemical detection chip. The integrated microfluidic channel controlledthe exact amount of solution to be delivered and provided real-time detection of DAS. The experimental results indicated that changes in channel current was proportional to DAS concentrations from 1 nM to 10 μM, with the detection limits of 0.8 nM. It is the first demonstration of the integration of a microfluidic system with a MIP-gated OECT for real-time tumor marker detection. The study results of this work helped to develop Point-Of-Care (POC) tools for the early diagnosis of lung cancer, colon cancer and breast cancer.

Identification of ASMTL-AS1 and LINC02604 lncRNAs as novel biomarkers for diagnosis of colorectal cancer

PurposeColorectal cancer is one of the major leading causes of death worldwide, and available treatments for advanced colorectal cancer are not successful. Therefore, early detection of colorectal cancer is essential to improve patient survival, and biomarkers are potential tools to achieve this goal. Considering the key role of lncRNAs in cancers, the aim of this study is to identify lncRNAs involved in colorectal cancer as new potential prognosis biomarkers for CRC.MethodsIn this observational study, gene expression data obtained from the TCGA database were analyzed, Identification of differentially expressed mRNAs, miRNAs, and lncRNAs was performed, and ceRNA network was drawn. Also, survival analysis of patients was performed in order to identify potential biomarkers related to the diagnosis and prognosis of colon cancer. After confirming the results using the GSE39582 dataset, the expression of target lncRNAs in colorectal tumor tissues was also investigated to confirm the bioinformatic data.ResultsAnalysis of the TCGA data showed that the expression of three lncRNAs—SNHG7, ASMTL-AS1, and LINC02604—that had the highest interaction with other miRNAs and mRNAs identified based on the ceRNA network was increased in colorectal cancer. Also, based on the ceRNA network, three microRNAs, hsa-let-7d-5p, hsa-mir-92a-3p, and hsa-mir-423-5p, and eight mRNAs, including CPA4, MSI2, RRM2, IGF2BP1, ONECUT2, HMGA1, SOX4, and SRM, were associated with all three mentioned lncRNAs, the expression of microRNAs was decreased and the expression of mRNAs was increased. By enrichment analysis, it was found that the target lncRNAs are involved in the processes of cell proliferation, apoptosis, and metastasis, indicating their importance in the development and malignancy of colorectal cancer. Furthermore, Kaplan–Meier analysis showed a significant increase in mortality in patients with higher expression levels of these lncRNAs. Analysis of the GSE39582 dataset, and real-time RT-PCR analysis, confirmed our bioinformatic results. Also, ROC analysis showed that SNHG7 was a relatively good promising biomarker (AUC = 0.73, p value = 0.02), while ASMTL-AS1 (AUC = 0.92, p value < 0.0001) and LINC02604 (AUC = 1.00, p value < 0.0001) emerged as excellent diagnostic biomarkers in colorectal cancer.ConclusionIt seems that increased expression of lncRNAs ASMTL-AS1 and LINC02604 can serve as molecular biomarkers for CRC, possibly through the sponge hsa-let-7d-5p, hsa-mir-92a-3p, and hsa-mir-423 5p, which increases target mRNAs, which are effective in the carcinogenesis process.

Patient and caregiver perspectives on causes and prevention of ambulatory adverse events: multilingual qualitative study

ContextAmbulatory adverse events (AEs) affect up to 25% of the global population and cause over 7 million preventable hospital admissions around the world. Though patients and caregivers are key actors...