Early Detection Of Colorectal Cancer Research Articles

Core-shell structures assembled from magnetic covalent organic frameworks as a Ti4+-fixed affinity substrate for the identification of phosphopeptides in colorectal cancer serum.

When it comes to the early detection of colorectal cancer (CRC), the study of protein phosphorylation is crucial. This work used magnetic covalent organic framework (COF) materials as substrates. The magCOF@PM-Ti4+ composites were obtained by constructing the three-dimensional cross-linked structures and chelating them with Ti4+ in a green and economical way, which was utilized to identify the phosphopeptides. This material possesses magnetic properties that facilitate simplified experimental manipulation, has superior sensitivity (0.2 fmol), excellent selectivity (1000:1), and good reusability (10 cycles), and achieves outstanding recovery rates (103.4 ± 1.2%). In addition, magCOF@PM-Ti4+ was utilized to identify phosphopeptides in human serum. One hundred phosphopeptides were identified from three normal control serums, whereas 98 phosphopeptides were identified from three CRC serums. The results demonstrated that magCOF@PM-Ti4+ has great potential in efficiently identifying low-abundance phosphopeptides.

The association between health literacy and colorectal cancer screening uptake in Taiwan

Abstract Background Population-based cancer screening programs have been confirmed as a cost-effective strategy for early detection of colorectal cancer, leading to reduced mortality and morbidity. In Taiwan, the government has provided an immunochemical fecal occult blood test (iFOBT) every two years to all citizens aged 50 to 74 years since 2013. The study aims to determine the association between iFOBT uptake and health literacy (HL). Methods Data from the National Health Interview Survey (NHIS), a population-based cross-sectional survey conducted in 2021-22 in Taiwan, were applied. The European Health Literacy Survey Short Form (HLS-EU-Q16), self-reported iFOBT participation within the past 2 years, and covariates assessed by face-to-face interview. The present analysis included those age-eligible for screening (n = 3,871) and applied the survey-weighted multivariable logistic regression to estimate odds of iFOBT uptake between HL skill levels, controlling for age, education attainment, socioeconomic status, health status, and household urbanicity level. Results In general, 44.6% of participants had utilized the iFOBT, and 79.9% had adequate HL skills which reported higher iFOBT uptake rates (45.9%) than their counterparts (39.4%). Multivariable analysis showed that having adequate HL was associated with greater odds of iFOBT utilization (adjusted odds ratio [aOR] = 1.79; 95% CI: 1.29-2.49). Additionally, compared to those households registered in the township, participants in higher urbanicity levels had lower iFOBT uptake: city (aOR = 0.62; 95% CI: 0.47-0.82). Other predictors that contributed significantly were participants’ gender and having 2 or more chronic diseases. Conclusions Inadequate HL skills and urban residency may reduce participation in the CRC screening program, despite its free availability. More efforts should include literacy-friendly designed materials and the provision of tailored support in urban settings. Key messages • HL skills are significantly associated with the CRC screening uptake in Taiwan. Improving HL could have positive effects on people’s participation. • Urbanicity levels are also predictors associated with screening utilization. Disseminating health service information in localized ways should be a potential option.

Potential of pre-diagnostic metabolomics for colorectal cancer risk assessment or early detection

This systematic review investigates the efficacy of metabolite biomarkers for risk assessment or early detection of colorectal cancer (CRC) and its precursors, focusing on pre-diagnostic biospecimens. Searches in PubMed, Web of Science, and SCOPUS through December 2023 identified relevant prospective studies. Relevant data were extracted, and the risk of bias was assessed with the QUADAS-2 tool. Among the 26 studies included, significant heterogeneity existed for case numbers, metabolite identification, and validation approaches. Thirteen studies evaluated individual metabolites, mainly lipids, while eleven studies derived metabolite panels, and two studies did both. Nine panels were internally validated, resulting in an area under the curve (AUC) ranging from 0.69 to 0.95 for CRC precursors and 0.72 to 1.0 for CRC. External validation was limited to one panel (AUC = 0.72). Metabolite panels and lipid-based biomarkers show promise for CRC risk assessment and early detection but require standardization and extensive validation for clinical use.

Colon polyp detection based on multi-scale and multi-level feature fusion and lightweight convolutional neural network

Early diagnosis and treatment of colorectal polyps are crucial for preventing colorectal cancer. This paper proposes a lightweight convolutional neural network for the automatic detection and auxiliary diagnosis of colorectal polyps. Initially, a 53-layer convolutional backbone network is used, incorporating a spatial pyramid pooling module to achieve feature extraction with different receptive field sizes. Subsequently, a feature pyramid network is employed to perform cross-scale fusion of feature maps from the backbone network. A spatial attention module is utilized to enhance the perception of polyp image boundaries and details. Further, a positional pattern attention module is used to automatically mine and integrate key features across different levels of feature maps, achieving rapid, efficient, and accurate automatic detection of colorectal polyps. The proposed model is evaluated on a clinical dataset, achieving an accuracy of 0.9982, recall of 0.9988, F1 score of 0.9984, and mean average precision (mAP) of 0.9953 at an intersection over union (IOU) threshold of 0.5, with a frame rate of 74 frames per second and a parameter count of 9.08 M. Compared to existing mainstream methods, the proposed method is lightweight, has low operating configuration requirements, high detection speed, and high accuracy, making it a feasible technical method and important tool for the early detection and diagnosis of colorectal cancer.

Advances in microRNAs as Emerging Biomarkers for Colorectal Cancer Early Detection and Diagnosis.

Colorectal cancer (CRC) remains the second most common cause of cancer-related mortality worldwide, necessitating advancements in early detection and innovative treatment strategies. MicroRNAs (miRNAs), small non-coding RNAs involved in gene regulation, have emerged as crucial players in the pathogenesis of CRC. This review synthesizes the latest findings on miRNA deregulated in precancerous lesions and in CRC. By examining the deregulation patterns of miRNAs across different stages of CRC development, this review highlights their potential as diagnostic tools. We specifically analyse the roles and diagnostic relevance of four miRNAs-miR-15b, miR-21, miR-31, and miR-146a-that consistently exhibit altered expression in CRC. The current knowledge of their role in key oncogenic pathways, drug resistance, and clinical relevance is discussed. Despite challenges posed by the heterogeneity of the research findings on miRNA deregulation and their role in CRC, integrating miRNA diagnostics into current screening methods holds promise for enhancing personalized medicine approaches. This review emphasizes the transformative potential of miRNAs in CRC diagnosis, paving the way for improved patient outcomes and novel therapeutic paradigms.

Robust prediction of colorectal cancer via gut microbiome 16S rRNA sequencing data.

Introduction. The study addresses the challenge of utilizing human gut microbiome data for the early detection of colorectal cancer (CRC). The research emphasizes the potential of using machine learning techniques to analyze complex microbiome datasets, providing a non-invasive approach to identifying CRC-related microbial markers.Hypothesis/Gap Statement. The primary hypothesis is that a robust machine learning-based analysis of 16S rRNA microbiome data can identify specific microbial features that serve as effective biomarkers for CRC detection, overcoming the limitations of classical statistical models in high-dimensional settings.Aim. The primary objective of this study is to explore and validate the potential of the human microbiome, specifically in the colon, as a valuable source of biomarkers for colorectal cancer (CRC) detection and progression. The focus is on developing a classifier that effectively predicts the presence of CRC and normal samples based on the analysis of three previously published faecal 16S rRNA sequencing datasets.Methodology. To achieve the aim, various machine learning techniques are employed, including random forest (RF), recursive feature elimination (RFE) and a robust correlation-based technique known as the fuzzy forest (FF). The study utilizes these methods to analyse the three datasets, comparing their performance in predicting CRC and normal samples. The emphasis is on identifying the most relevant microbial features (taxa) associated with CRC development via partial dependence plots, i.e. a machine learning tool focused on explainability, visualizing how a feature influences the predicted outcome.Results. The analysis of the three faecal 16S rRNA sequencing datasets reveals the consistent and superior predictive performance of the FF compared to the RF and RFE. Notably, FF proves effective in addressing the correlation problem when assessing the importance of microbial taxa in explaining the development of CRC. The results highlight the potential of the human microbiome as a non-invasive means to detect CRC and underscore the significance of employing FF for improved predictive accuracy.Conclusion. In conclusion, this study underscores the limitations of classical statistical techniques in handling high-dimensional information such as human microbiome data. The research demonstrates the potential of the human microbiome, specifically in the colon, as a valuable source of biomarkers for CRC detection. Applying machine learning techniques, particularly the FF, is a promising approach for building a classifier to predict CRC and normal samples. The findings advocate for integrating FF to overcome the challenges associated with correlation when identifying crucial microbial features linked to CRC development.

B-330 Reducing Inappropriate Fecal Immunochemical Test

Abstract Background Colorectal cancer is the third most common cancer worldwide. The risk of colorectal cancer increases with age and predominantly affects individuals over the age of 50. Regular screening is crucial for early detection and prevention of colorectal cancer. Pre-cancerous polyps and colorectal cancers can bleed into the gastrointestinal tract. While the presence of blood in the stool cannot always be seen visually, it can be detected by the Fecal Immunochemical Test (FIT), currently the most sensitive method for directly measuring hemoglobin in this specimen type. In our province, FIT is recommended for testing average-risk asymptomatic patients aged 50 to 74 every 1-2 years. Patients aged 40 to 49 or 75 to 84 can be tested if they meet specific criteria. Like many other tests in the lab, FIT can be used inappropriately. The focus of this study was to examine the utilization of FIT in our institution. Methods Data were collected from our institution's laboratory information system for the FIT performed on-site from July to December 2023. Results We performed 91,545 FIT tests during the 6-month period with an overall positive rate of 6.8%. Of the total tests performed, 87.9% were for patients aged 50-74 years, 10.6% for patients aged 40-49 or 75-84 years, and 1.2% for patients outside the approved age range. Each year, we performed over 2,000 FIT tests for patients outside the accepted screening age, with an estimated cost of over 30,000 CAD. Conclusions Our results showed a significant number of FITs were ordered inappropriately by healthcare providers. Considering the relatively high cost of FIT kits, labor, reagents, etc., the overall cost per reportable is significant. Thus, we have started engaging healthcare providers involved in FIT testing to strategize approaches to improve utilization.

Application of Mendelian randomization analysis in investigating the genetic background of blood biomarkers for colorectal cancer.

Colorectal cancer (CRC), a malignancy affecting the colon and rectum, ranks as the third most common cancer worldwide and the second leading cause of cancer-related deaths. Early detection of CRC is crucial for preventing metastasis, reducing mortality, improving prognosis, and enhancing patients' quality of life. Genetic factors play a significant role in CRC development, accounting for up to 35% of the disease risk. Genome-wide association studies have identified several genetic loci associated with CRC risk. However, these studies often lack direct evidence of causality. While traditional blood biomarkers such as carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) are widely used for CRC diagnosis and monitoring, their sensitivity and accuracy in early diagnosis are limited. Thus, there is a pressing need to develop new biomarkers that reflect the genetic background of CRC to improve early detection and diagnostic accuracy. In addition, understanding the genetic mechanisms underlying these biomarkers is essential for elucidating CRC pathogenesis and developing precise personalized treatment strategies. Mendelian randomization (MR) analysis, as an emerging epidemiological tool, can accurately assess the causal relationship between genetic variations and diseases by reducing confounding biases in observational studies. MR analysis has been applied in evaluating the causal impact of various blood biomarkers on CRC risk, shedding lights on the potential causal relationships between these biomarkers and CRC pathogenesis in the context of genetic background. In this review, we summarize the applications of MR analysis in studies of blood biomarkers for CRC, aiming to enhance the early diagnosis and personalized treatment of CRC.

Efficacy of ColonFlag as a Complete Blood Count-Based Machine Learning Algorithm for Early Detection of Colorectal Cancer: A Systematic Review.

Colorectal cancer (CRC) screening is essential to reduce incidence and mortality rates. However, participation in screening remains suboptimal. ColonFlag, a machine learning algorithm using complete blood count (CBC), identifies individuals at high CRC risk using routinely performed tests. This study aims to review the existing literature assessing the efficacy of ColonFlag across diverse populations in multiple countries. The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) were followed in reporting this systematic review. Searches were conducted on PubMed, Cochrane, ScienceDirect, and Google Scholar for English articles, using keywords related to CBC, machine learning, ColonFlag, and CRC, covering the first development study from 2016 to August 2023. The Cochrane Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias. A total of 949 articles were identified during the literature search. Ten studies were found to be eligible. ColonFlag yielded Area Under the Curve (AUC) values ranging from 0.736 to 0.82. The sensitivity and specificity ranged from 3.91% to 35.4% and 82.73% to 94%, respectively. The positive predictive values ranged between 2.6% and 9.1%, while the negative predictive values ranged from 97.6% to 99.9%. ColonFlag performed better in shorter time windows, tumors located more proximally, in advanced stages, and in cases of CRC compared to adenoma. While ColonFlag exhibits low sensitivity compared to established screening methods such as the fecal immunochemical test (FIT) or colonoscopy, its potential to detect CRC before clinical diagnosis suggests an opportunity for identifying more cases than regular screening alone.

The Next Frontier for Colorectal Cancer Screening: Blood-Based Tests.

Colorectal cancer is associated with substantial morbidity and mortality worldwide. Detection of early colorectal cancer is possible through approved screening tests but overall adherence is quite low. Implementation of effective noninvasive options could increase screening uptake and prevent a significant number of deaths. Noninvasive early cancer detection can potentially be achieved using a liquid biopsy. In this issue of Cancer Research, Cao and colleagues report on a novel multidimensional fragmentomics assay, named FRAGTECT, for colorectal cancer detection in circulating cell-free DNA with promising results. See related article by Cao et al., p. 3286.

S558 Test Performance and Lesion Characteristics in a Large Clinical Validation Study of a Blood-Based Screening Test for the Early Detection of Colorectal Cancer

S558 Test Performance and Lesion Characteristics in a Large Clinical Validation Study of a Blood-Based Screening Test for the Early Detection of Colorectal Cancer

Multidimensional Fragmentomics Enables Early and Accurate Detection of Colorectal Cancer.

Colorectal cancer is frequently diagnosed in advanced stages, highlighting the need for developing approaches for early detection. Liquid biopsy using cell-free DNA (cfDNA) fragmentomics is a promising approach, but the clinical application is hindered by complexity and cost. This study aimed to develop an integrated model using cfDNA fragmentomics for accurate, cost-effective early-stage colorectal cancer detection. Plasma cfDNA was extracted and sequenced from a training cohort of 360 participants, including 176 patients with colorectal cancer and 184 healthy controls. An ensemble stacked model comprising five machine learning models was employed to distinguish patients with colorectal cancer from healthy controls using five cfDNA fragmentomic features. The model was validated in an independent cohort of 236 participants (117 patients with colorectal cancer and 119 controls) and a prospective cohort of 242 participants (129 patients with colorectal cancer and 113 controls). The ensemble stacked model showed remarkable discriminatory power between patients with colorectal cancer and controls, outperforming all base models and achieving a high area under the receiver operating characteristic curve of 0.986 in the validation cohort. It reached 94.88% sensitivity and 98% specificity for detecting colorectal cancer in the validation cohort, with sensitivity increasing as the cancer progressed. The model also demonstrated consistently high accuracy in within-run and between-run tests and across various conditions in healthy individuals. In the prospective cohort, it achieved 91.47% sensitivity and 95.58% specificity. This integrated model capitalizes on the multiplex nature of cfDNA fragmentomics to achieve high sensitivity and robustness, offering significant promise for early colorectal cancer detection and broad patient benefit. Significance: The development of a minimally invasive, efficient approach for early colorectal cancer detection using advanced machine learning to analyze cfDNA fragment patterns could expedite diagnosis and improve treatment outcomes for patients. See related commentary by Rolfo and Russo, p. 3128.

5-Hydroxymethylated Biomarkers in Cell-Free DNA Predict Occult Colorectal Cancer up to 36 Months Before Diagnosis in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial.

Using the prostate, lung, colorectal, and ovarian (PLCO) Cancer Screening Trial samples, we identified cell-free DNA (cfDNA) candidate biomarkers bearing the epigenetic mark 5-hydroxymethylcytosine (5hmC) that detected occult colorectal cancer (CRC) up to 36 months before clinical diagnosis. We performed the 5hmC-seal assay and sequencing on ≤8 ng cfDNA extracted from PLCO study participant plasma samples, including n = 201 cases (diagnosed with CRC within 36 months of blood collection) and n = 401 controls (no cancer diagnosis on follow-up). We conducted association studies and machine learning modeling to analyze the genome-wide 5hmC profiles within training and validation groups that were randomly selected at a 2:1 ratio. We successfully obtained 5hmC profiles from these decades-old samples. A weighted Cox model of 32 5hmC-modified gene bodies showed a predictive detection value for CRC as early as 36 months before overt tumor diagnosis (training set AUC, 77.1% [95% CI, 72.2 to 81.9] and validation set AUC, 72.8% [95% CI, 65.8 to 79.7]). Notably, the 5hmC-based predictive model showed comparable performance regardless of sex and race/ethnicity, and significantly outperformed risk factors such as age and obesity (assessed as BMI). Finally, when splitting cases at median weighted prediction scores, Kaplan-Meier analyses showed significant risk stratification for CRC occurrence in both the training set (hazard ratio, [HR], 3.3 [95% CI, 2.6 to 5.8]) and validation set (HR, 3.1 [95% CI, 1.8 to 5.8]). Candidate 5hmC biomarkers and a scoring algorithm have the potential to predict CRC occurrence despite the absence of clinical symptoms and effective predictors. Developing a minimally invasive clinical assay that detects 5hmC-modified biomarkers holds promise for improving early CRC detection and ultimately patient outcomes.

Awareness of Colorectal Cancer Preventive Measures Among Residents of Riyadh.

Background Colorectal cancer (CRC) poses a significant challenge to healthcare providers. Spreading awareness, providing preventive measures, and implementing screening programs are essential for detecting and halting the progression of the disease and decreasing the mortality associated with this type of malignancy. This study aimed to assess public knowledge and awareness of colorectal cancer preventive measures and screening programs among residents of Riyadh, Saudi Arabia, and also to design better healthcare interventions or policy development. Methods This cross-sectional study was carried out among residents in Riyadh, Saudi Arabia. A self-administered questionnaire was sent to participants using a Google survey. The questionnaire consisted of socio-demographic characteristics (age, gender, marital status, and occupation), a general understanding of CRC and its prevention, risk factors for developing CRC, barriers to undergoing CRC screening, and a 21-item questionnaire to assess awareness of CRC preventive measures. Results Of the 420 respondents, 301 (71.7%) were female, and 134 (31.9%) were between 18 and 29 years old. The overall mean awareness score was 12.5 (SD 3.05) out of 21 points. Accordingly, 240 (57.1%) were categorized as having moderate awareness, 70 (16.7%) as good, and 110 (26.2%) had poor awareness levels. Being younger, being unmarried, having heard of CRC and screening tests that detect colon cancer, and being aware of the CRC early detection campaign in Saudi Arabia were the factors associated with increased awareness. Conclusion There was modest awareness of CRC preventive measures among residents living in Riyadh. Significant predictors of increased awareness include younger age, being unmarried, having heard of CRC, having heard of screening tests to detect CRC, and awareness of the CRC early-detection campaigns in Saudi Arabia. Healthcare providers have a vital role in increasing awareness of CRC's preventive measures.

Correlation between gut microbiota dysbiosis and colorectal cancer: review

Introduction and Purpose: The human gut microbiota, comprising a diverse consortium of approximately 100 trillion microorganisms, is integral to maintaining health and modulating disease processes. Its development begins at birth, influenced by maternal microbiota and environmental factors. Dysbiosis, defined as an imbalance in the gut microbial composition, has been implicated in a range of gastrointestinal pathologies, including colorectal cancer (CRC). This review endeavors to elucidate the relationship between gut microbiota and CRC, examining the impact of specific bacterial taxa on the pathogenesis CRC. Description of the State of Knowledge: Gut microbiota encompasses a multitude of microbial species, with their composition differing along the gastrointestinal tract. Healthy gut microbiota perform essential functions such as pathogen protection, metabolic processes, and immune system modulation. Factors like diet and genetics significantly influence microbial composition. Dysbiosis contributes to CRC through inflammation, genotoxin production, and immune modulation. Specific bacteria, such as Fusobacterium nucleatum and Bacteroides fragilis, are associated with CRC. Mechanisms of carcinogenesis include bacterial adherence, invasion of epithelial cells, and activation of pro-inflammatory pathways. Summary:Understanding the gut microbiota's role in CRC highlights the importance of maintaining a balanced microbiome for cancer prevention. Dietary interventions promoting beneficial bacteria and reducing harmful species could mitigate CRC risk. Further research should prioritize the identification of microbial biomarkers for early CRC detection and the development of therapeutic strategies aimed at modulating the gut microbiota. These efforts will enhance CRC prevention and treatment modalities.

CD24-Targeted NIR-II Fluorescence Imaging Enables Early Detection of Colorectal Neoplasia.

Colorectal cancer (CRC) continues to be a major health issue even though screening methods have facilitated early detection. Despite the high sensitivity of white-light colonoscopy, it frequently overlooks invasive flat or depressed lesions, which can lead to the development of larger, advanced tumors. Fluorescence molecular imaging (FMI) offers a promising approach for early tumor detection by targeting specific molecular characteristics of lesions. CD24 is upregulated during the adenoma-to-CRC transition, providing a potential target for FMI. Here, we developed a second near-infrared window (NIR-II) fluorescent probe with a high affinity for CD24 and evaluated its efficacy and targeting ability in cellular models, murine models, and clinical samples of CRC. CD24 expression was elevated in 76% of adenomas and 80% of CRCs. In a colitis-associated cancer mouse model, NIR-II imaging with the CD24-targeted probe achieved a significantly higher tumor-to-background ratio compared to conventional NIR-I imaging. The probe demonstrated exceptional sensitivity (92%) and specificity (92%) for detecting CRC, including small lesions less than 1 mm in size. This led to the identification of precancerous lesions missed by white-light detection and lesions missed by NIR-I imaging. Moreover, ex vivo human tissue incubation with the probe supported the potential for intraprocedural lesion identification via topical probe application during colonoscopy. In conclusion, this study successfully demonstrates the potential of CD24-targeted NIR-II imaging for identifying colorectal neoplasia, highlighting its significance for early CRC detection in the gastrointestinal tract.

National Colorectal Cancer Early Detection Program: overview of experience gained over the last seventeen years (2007-2024)

National Colorectal Cancer Early Detection Program: overview of experience gained over the last seventeen years (2007-2024)

Identification and evaluation of a serum microRNA panel to diagnose colorectal cancer patients.

Screening plays a crucial role in the early detection of colorectal cancer, greatly reducing mortality rates. The objective of this study was to identify a non-invasive diagnostic method utilizing serum microRNA expression for the diagnosis of colorectal cancer patients. The study consisted of three stages. In the first stage, 129 patients with colorectal cancer and 129 normal subjects were recruited as the training set for the development of a blood miRNA panel. The second stage involved recruiting 200 patients from each group as the validation cohort. Finally, a blinded study was conducted in the third stage, with 260 patients recruited to determine the predictive value of our miRNA panel. Serum samples were prospectively collected from colorectal cancer patients and normal subjects between 2017 and 2021 at Queen Mary Hospital in Hong Kong. Quantitative PCR was utilized to detect the serum levels of candidate microRNAs, and a multiple linear regression model was employed to formulate a serum microRNA panel for diagnosing colorectal cancer patients. The performance of the panel was evaluated using ROC analysis. Our study showed that the values of three pairs of serum microRNAs, namely miR-106b-5p/miR-1246, miR-106b-5p/miR-16 and miR-106b-5p/miR-21-5p, exhibited statistically significant differences between colorectal cancer patients and normal subjects. A serum microRNA panel formulated from these three pairs of microRNAs demonstrated high accuracy in diagnosing colorectal cancer patients from normal subjects, with an AUC of approximately 0.9. The serum miRNA test proved to be a feasible and promising non-invasive biomarker for the diagnosis of colorectal cancer patients in comparison to normal subjects.

Differential Outcomes in Colorectal Cancer Detection: A Comparative Study of Swedish Nationwide Screening and Fast-Track Diagnostic Pathways.

In 2021, a nation-wide screening program for colorectal cancer (CRC) was step-wise implemented in Region Örebro County (RÖC) for patients aged 60 to 74 years, utilizing the fecal immunochemical test (FIT) to refer patients for colonoscopy. Concurrently, the standardized care course for colorectal cancer (SCC-CRC), initiated in 2016, employs a fast-track pathway for patients with alarm symptoms to undergo colonoscopy. This study compares CRC screening colonoscopies with SCC-CRC colonoscopies in RÖC among patients aged 60 to 67 years. An initial analysis of the Swedish colorectal screening cohort was combined with a retrospective cohort study, analyzing data from 307 CRC screening patients and 441 age-matched SCC-CRC patients in RÖC. Data included demographics, colonoscopy participation rates, and pathology findings. Statistical analyses compared outcomes between the 2 groups. Among the screening group, 2% tested positive for FIT, with an 86% colonoscopy participation rate (N=9296). In RÖC, 266 screening patients underwent colonoscopy, with 10% diagnosed with CRC, compared with 20% in the SCC-CRC group. In addition, 39% of the screening group in RÖC were diagnosed with advanced adenomas, versus 15% in the SCC-CRC group. Screening participation was high, with effectiveness aligning with international counterparts. The SCC-CRC pathway excels in diagnosing CRC among symptomatic patients, while the nationwide screening program is effective in early detection of CRC and advanced adenomas. underscoring the importance of integrating and optimizing both approaches within the Swedish health care system to optimize CRC prevention and management.

SERS Analysis Platform Based on Aptamer Recognition-Release Strategy for Efficient and Sensitive Diagnosis of Colorectal Precancerous Lesions.

Colorectal cancer (CRC) has become a significant global public health challenge, demanding immediate attention due to its high incidence and mortality rates. Regular CRC screening is essential for the early detection of precancerous lesions and CRC. : We developed a novel surface-enhanced Raman scattering (SERS) analysis platform that employs high-throughput microarray chips as carriers and Au/SnO2 nanoring arrays (Au/SnO2 NRAs) as substrates. This platform utilizes an aptamer recognition-release strategy to achieve efficient and sensitive detection of protein tumor markers. In the detection process, the strong affinity and high specificity between the aptamer and the target protein result in competitive replacement of the SERS nanoprobes originally bound to the substrate surface. As a result, the SERS nanoprobes carrying Raman reporter genes are dislodged, leading to a reduction in the SERS signal intensity. The platform demonstrated excellent detection performance, with rapid detection completed within 15minutes and limits of detection (LOD) as low as 6.2×10-12 g/mL for hnRNP A1 and 6.51×10-12 g/mL for S100P. Clinical samples analyzed using the SERS platform showed high consistency with enzyme-linked immunosorbent assay (ELISA) results. This platform offers strong support for the early detection, risk assessment, and treatment monitoring of colorectal cancer precancerous lesions, with broad potential for clinical applications.