Colorectal cancer diagnosis: Pitfalls and opportunities

Exploring colorectal cancer patients' diagnostic pathways and general practitioners' assessment of the diagnostic processes: a Danish survey study

INTRODUCTION

Colorectal cancer (CRC) is among the most common cancers and the prognosis of CRC is highly dependent on stage at diagnosis. Although many cases are diagnosed swiftly, there is still room for improvement.

AIM

We aimed to explore CRC diagnostic pathways, encompassing (1) place of initial contact; (2) associations with symptom presentations, sex, and age with events in the diagnostic process and initial referrals and (3) the general practitioner's (GP's) evaluation of the diagnostic processes.

METHODS

All GPs in North-, Central-, and Southern Denmark were invited to fill in questionnaires for their listed patients diagnosed with cancer during the past two years.

RESULTS

Among 1,032 recorded CRC patients, 65% had their initial contact in general practice, 5% within the out-of hours service, 10% in the hospital, and 20% were diagnosed based on screening. A total of 27% of CRC patients over 40 who initially presented in general practice were treated or referred on suspicion of another disease first, and 9% were reported to have had hesitated in seeking medical attention. Some 37% presented solely non-specific symptoms, increasing the odds of the GP advising watchful waiting (OR 2.48; 95% CI 1.06-5.81), treating or referring on the suspicion of another illness first (OR 2.57; 95% CI 1.76-3.75), wait due to normal findings (OR 2.11; 95% CI 1.16-3.85), or referring to diagnostic imaging (OR 3.07; 95% CI 1.63-5.79). The GPs assessed nearly one fifth of the diagnostic processes as poor.

CONCLUSION

Most CRC patients are diagnosed with initial presentation in general practice. Having non-specific symptoms is common and challenges timely diagnosis.

The Effect of C-X-C Motif Chemokine Ligand 12 in Colorectal Cancer Associated with Chemoresistance and Radioresistance as Well as Stemness

Background

We aimed to explore the role of C-X-C motif chemokine ligand 12 (CXCL12) and cytokinecytokine receptor interaction signaling pathway in the radiotherapy and chemotherapy resistance as well as cell stemness in colorectal cancer (CRC).

Methods

Bioinformatics analysis was used to identify the differentially expressed mRNAs and signal pathways closely related to differentially expressed mRNAs have also been analyzed in March 2022 at the Jinhua Central Hospital, China. Then, the expression of CXCL12 was detected by qRT-PCR in colorectal cancer cells and testing the effects of transfecting CXCL12 into different CRC-derived cell lines. The effects of CXCL12 on cell proliferation were evaluated by chemosensitivity assay and radiation sensitivity assay.

Results

Bioinformatics analysis of DEGs found a total of 2429 differentially expressed genes, THBS3 and CXCL12 genes are two abnormally highly expressed genes in the CRC. KEGG analysis showed the correlative signaling pathway, cytokine-cytokine receptor interaction, which is related to cell stemness. Furthermore, the expression of CXCL12 in CRC cells was detected and an increasing trend was obtained in CRC cells. In addition, the chemosensitivity and radiotherapy tolerance were elevated after transfected with CXCL12.

Conclusion

CXCL12 could be a potential promote biomarkers in CRC and also promote the chemosensitivity and radiotherapy tolerance.

Risk factors for early-onset colorectal cancer: A Canadian prospective cohort study

PURPOSE

The incidence of early-onset (<50 years of age) colorectal cancer (eoCRC) has been steadily increasing in high-income countries including Canada. Despite this increase in incidence, the etiology of eoCRC remains unclear and prospective cohort studies of potential risk factors are limited.

METHODS

We examined two prospective cohorts of healthy individuals (<50 years of age) who completed baseline questionnaires in the Ontario Health Study and Alberta's Tomorrow Project. We examined the associations between demographic characteristics, chronic health conditions, and lifestyle behaviours with the development of eoCRC using Cox proportional hazard models. Cohorts were analyzed separately and hazard ratios for each risk factor were pooled with random effects meta-analyses.

RESULTS

During an average follow-up of 6.63 years, 98 eoCRC cases occurred among study participants (n=127,852). A family history of CRC alone or with a history of other cancer types was associated with an increased risk of developing eoCRC (HR: 2.76, 95% CI: 1.43-5.32), but a family history of a non-CRC cancer only was not (HR: 1.18, 95% CI: 0.61-2.30). Heavy smokers (≥ 10 pack-years) at baseline had a higher risk of eoCRC compared to non-smokers (HR: 1.87, 95% CI: 1.00-3.52). Sex, socioeconomic factors, diabetes, alcohol consumption, among other factors were not significantly associated with the risk of eoCRC.

CONCLUSION

Our findings indicate that specific CRC risk factors are also associated with developing eoCRC. The data in the study offers valuable insights that could be integrated in future meta-analyses. Additional prospective cohort studies are required to understand the etiology of eoCRC.

Understanding Colorectal Cancer Patient Experiences with Family Practitioners in Canada

Despite ongoing screening efforts, colorectal cancer (CRC) remains a leading cause of death in Canada. The aim of this study was to better understand the experiences of Canadian CRC patients with their family practitioners (FPs) during and after their CRC diagnosis. Patient-reported data were collected through an online questionnaire to understand their CRC diagnosis experiences and identify potential gaps in care. Various factors contributing to challenges throughout a patient's CRC diagnosis (e.g., delayed CRC diagnosis) were determined using descriptive, qualitative, and inferential analyses. These factors could be targeted to optimize CRC care. This study found that 40.6% of the 175 respondents were unaware of at least one of the following aspects of CRC prior to their diagnosis: early-age onset (EAO), symptoms, and screening procedures. While 84.6% had access to a family physician (FP) before their diagnosis, only 17.7% were diagnosed by FPs. Higher proportions of younger individuals experienced misdiagnoses and felt dismissed compared to older individuals. Only half felt fully informed about their diagnosis when it was explained to them by their FP, while 53.1% had their diagnosis explained in plain language. Transitioning towards patient-centred care would promote pre-diagnosis CRC awareness, address differences in management of CRC care (e.g., dismissal and support), and accommodate for age and health-literacy-related disparities, thereby improving CRC care pathways for patients. Future research should investigate FPs experiences in detecting CRC cases to develop educational resources and recommendations, enhancing early detection and improving patient outcomes (1).

Examining variations in body composition among patients with colorectal cancer according to site and disease stage

Patients with colorectal cancer (CRC) often exhibit changes in body composition (BC) which are associated with poorer clinical outcomes. Many studies group colon and rectal cancers together, irrespective of staging, potentially affecting assessment and treatment strategies. Our study aimed to compare BC in patients with CRC focusing on tumor location and metastasis presence. A total of 635 individuals were evaluated, with a mean age of 61.8 ± 12.4 years and 50.2% female. The majority had rectal cancer as the primary cancer site (51.0%), and 23.6% had metastatic disease. The first regression model showed tumor site and metastasis as independent factors influencing skeletal muscle (SM), skeletal muscle index (SMI), and visceral adipose tissue variability (all p values < 0.05). The second model, adjusted for BMI, indicated tumor site as the primary factor affecting SMI variations (adjusted R2 = 0.50 p < 0.001), with colon tumors inversely associated with SM (standardized β - 2.15(- 3.3; - 0.9) p < 0.001). A third model, considering all the confounders from the directed acyclic graphs, was constructed and the found association remained independent. Our findings highlight significant BC variations in patients with CRC, influenced by tumor location and metastases presence, underscoring the need for location-specific assessment in CRC management.

Distinct plasma molecular profiles between early-onset and late-onset colorectal cancer patients revealed by metabolic and lipidomic analyses

Colorectal cancer (CRC) incidence in younger adults has been steadily rising, warranting an in-depth investigation into the distinctions between early-onset CRC (EOCRC, < 50 years) and late-onset CRC (LOCRC, ≥ 50 years). Despite extensive study of clinical, pathological, and molecular traits, differentiating EOCRC from LOCRC and identifying potential biomarkers remain elusive. We analyzed plasma samples from healthy individuals, EOCRC, and LOCRC patients using liquid-chromatography mass spectrometry (LC/MS)-based metabolomics and lipidomics. Distinct polar metabolite and lipid profiles with significant metabolites altered in CRC group (e.g., choline and DG 40:4) were identified. Notably, EOCRC exhibited distinct polar metabolomic and differential lipidomic profiles compared to LOCRC, with polar metabolites like aminoadipate and uridine contributing significantly to the difference, and originating from pathways such as lysine biosynthesis and nucleotide metabolism. Furthermore, gene set enrichment analysis (GSEA) using independent TCGA gene expression data identified pathways significantly enriched in either EOCRC or LOCRC. Integrating gene expression and metabolomics data revealed numerous associations differentiating EOCRC and LOCRC. Our multi-omics integration underscores critical molecular distinctions, offers insights into the EOCRC development mechanisms and potential plasma biomarkers for diagnosis.

A multi-dimensional approach to unravel the intricacies of lactylation related signature for prognostic and therapeutic insight in colorectal cancer

BACKGROUND

Lactylation, a novel contributor to post-translational protein modifications, exhibits dysregulation across various tumors. Nevertheless, its intricate involvement in colorectal carcinoma, particularly for non-histone lactylation and its intersection with metabolism and immune evasion, remains enigmatic.

METHODS

Employing immunohistochemistry on tissue microarray with clinical information and immunofluorescence on colorectal cell lines, we investigated the presence of global lactylation and its association with development and progression in colorectal cancer as well as its functional location. Leveraging the AUCell algorithm alongside correlation analysis in single-cell RNA sequencing data, as well as cox-regression and lasso-regression analysis in TCGA dataset and confirmed in GEO dataset, we identified a 23-gene signature predicting colorectal cancer prognosis. Subsequently, we analyzed the associations between the lactylation related gene risk and clinical characteristics, mutation landscapes, biological functions, immune cell infiltration, immunotherapy responses, and drug sensitivity. Core genes were further explored for deep biological insights through bioinformatics and in vitro experiments.

RESULTS

Our study innovatively reveals a significant elevation of global lactylation in colorectal cancer, particularly in malignant tumors, confirming it as an independent prognostic factor for CRC. Through a comprehensive analysis integrating tumor tissue arrays, TCGA dataset, GEO dataset, combining in silico investigations and in vitro experiments, we identified a 23-gene Lactylation-Related Gene risk model capable of predicting the prognosis of colorectal cancer patients. Noteworthy variations were observed in clinical characteristics, biological functions, immune cell infiltration, immune checkpoint expression, immunotherapy responses and drug sensitivity among distinct risk groups.

CONCLUSIONS

The Lactylation-Related Gene risk model exhibits significant potential for improving the management of colorectal cancer patients and enhancing therapeutic outcomes, particularly at the intersection of metabolism and immune evasion. This finding underscores the clinical relevance of global lactylation in CRC and lays the groundwork for mechanism investigation and targeted therapeutic strategies given the high lactate concentration in CRC.

Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges

The use of data-driven high-throughput analytical techniques, which has given rise to computational oncology, is undisputed. The widespread use of machine learning (ML) and mathematical modeling (MM)-based techniques is widely acknowledged. These two approaches have fueled the advancement in cancer research and eventually led to the uptake of telemedicine in cancer care. For diagnostic, prognostic, and treatment purposes concerning different types of cancer research, vast databases of varied information with manifold dimensions are required, and indeed, all this information can only be managed by an automated system developed utilizing ML and MM. In addition, MM is being used to probe the relationship between the pharmacokinetics and pharmacodynamics (PK/PD interactions) of anti-cancer substances to improve cancer treatment, and also to refine the quality of existing treatment models by being incorporated at all steps of research and development related to cancer and in routine patient care. This review will serve as a consolidation of the advancement and benefits of ML and MM techniques with a special focus on the area of cancer prognosis and anticancer therapy, leading to the identification of challenges (data quantity, ethical consideration, and data privacy) which are yet to be fully addressed in current studies.

Managing Colorectal Cancer from Ethology to Interdisciplinary Treatment: The Gains and Challenges of Modern Medicine

Colorectal cancer (CRC) is a common malignant tumor of the gastrointestinal tract, which has become a serious threat to human health worldwide. This article exhaustively reviews colorectal cancer's incidence and relevance, carcinogenesis molecular pathways, up-to-date treatment opportunities, prophylaxis, and screening program achievements, with attention paid to its regional variations and changes over time. This paper provides a concise overview of known CRC risk factors, including familial, hereditary, and environmental lifestyle-related risk factors. The authors take a closer look into CRC's molecular genetic pathways and the role of specific enzymes involved in carcinogenesis. Moreover, the role of the general practitioner and multidisciplinary approach in CRC treatment is summarized and highlighted based on recent recommendations and experience. This article gives a clear understanding and review of the gains and challenges of modern medicine towards CRC. The authors believe that understanding the current patterns of CRC and its revolution is imperative to the prospects of reducing its burden through cancer prevention and cancer-adjusted treatment.

Enhanced Chemo-Immunotherapy Strategy Utilizing Injectable Thermosensitive Hydrogel for The Treatment of Diffuse Peritoneal Metastasis in Advanced Colorectal Cancer

Patients with colorectal cancer (CRC) and diffuse peritoneal metastasis (PM) are not eligible for surgical intervention. Thus, palliative treatment remains the standard of care in clinical practice. Systemic chemotherapy fails to cause drug accumulation at the lesion sites, while intraperitoneal chemotherapy (IPC) is limited by high clearance rates and associated complications. Given the poor prognosis, a customized OxP/R848@PLEL hydrogel delivery system has been devised to improve the clinical benefit of advanced CRC with diffuse PM. This system is distinguished by its simplicity, security, and efficiency. Specifically, the PLEL hydrogel exhibits excellent injectability and thermosensitivity, enabling the formation of drug depots within the abdominal cavity, rendering it an optimal carrier for IPC. Oxaliplatin (OxP), a first-line drug for advanced CRC, is cytotoxic and enhances the immunogenicity of tumors by inducing immunogenic cell death. Furthermore, OxP and resiquimod (R848) synergistically enhance the maturation of dendritic cells, promote the expansion of cytotoxic T lymphocytes, and induce the formation of central memory T cells. Moreover, R848 domesticates macrophages to an anti-tumor phenotype. OxP/R848@PLEL effectively eradicates peritoneal metastases, completely inhibits ascites production, and significantly prolongs mice lifespan. As such, it provides a promising approach to managing diffuse PM in patients with CRC without surgical indications.

Sociodemographic variations in the uptake of faecal immunochemical tests in primary care: a retrospective study

BACKGROUND

Faecal immunochemical test (FIT) usage for symptomatic patients is increasing, but variations in use caused by sociodemographic factors are unknown. A clinical pathway for colorectal cancer (CRC) was introduced in primary care for symptomatic patients in November 2017. The pathway was commissioned to provide GPs with direct access to FITs.

AIM

To identify whether sociodemographic factors affect FIT return in symptomatic patients.

DESIGN AND SETTING

A retrospective study was undertaken in Nottingham, UK, following the introduction of FIT as triage tool in primary care. It was mandated for all colorectal referrals (except rectal bleeding or mass) to secondary care. FIT was used, alongside full blood count and ferritin, to stratify CRC risk.

METHOD

All referrals from November 2017 to December 2021 were retrospectively reviewed. Sociodemographic factors affecting FIT return were analysed by multivariate logistic regression.

RESULTS

A total of 35 289 (90.7%) patients returned their index FIT, while 3631 (9.3%) did not. On multivariate analysis, males were less likely to return an FIT (odds ratio [OR] 1.11, 95% confidence interval [CI] = 1.03 to 1.19). Patients aged ≥65 years were more likely to return an FIT (OR 0.78 for non-return, 95% CI = 0.72 to 0.83). Unreturned FIT more than doubled in the most compared with the least deprived quintile (OR 2.20, 95% CI = 1.99 to 2.43). Patients from Asian (OR 1.82, 95% CI = 1.58 to 2.10), Black (OR 1.21, 95% CI = 0.98 to 1.49), and mixed or other ethnic groups (OR 1.29, 95% CI = 1.05 to 1.59) were more likely to not return an FIT compared with patients from a White ethnic group. A total of 599 (1.5%) CRCs were detected; 561 in those who returned a first FIT request.

CONCLUSION

FIT return in those suspected of having CRC varied by sex, age, ethnic group, and socioeconomic deprivation. Strategies to mitigate effects on FIT return and CRC detection should be considered as FIT usage expands.

A Novel Heteromorphous Convolutional Neural Network for Automated Assessment of Tumors in Colon and Lung Histopathology Images

The automated assessment of tumors in medical image analysis encounters challenges due to the resemblance of colon and lung tumors to non-mitotic nuclei and their heteromorphic characteristics. An accurate assessment of tumor nuclei presence is crucial for determining tumor aggressiveness and grading. This paper proposes a new method called ColonNet, a heteromorphous convolutional neural network (CNN) with a feature grafting methodology categorically configured for analyzing mitotic nuclei in colon and lung histopathology images. The ColonNet model consists of two stages: first, identifying potential mitotic patches within the histopathological imaging areas, and second, categorizing these patches into squamous cell carcinomas, adenocarcinomas (lung), benign (lung), benign (colon), and adenocarcinomas (colon) based on the model's guidelines. We develop and employ our deep CNNs, each capturing distinct structural, textural, and morphological properties of tumor nuclei, to construct the heteromorphous deep CNN. The execution of the proposed ColonNet model is analyzed by its comparison with state-of-the-art CNNs. The results demonstrate that our model surpasses others on the test set, achieving an impressive F1 score of 0.96, sensitivity and specificity of 0.95, and an area under the accuracy curve of 0.95. These outcomes underscore our hybrid model's superior performance, excellent generalization, and accuracy, highlighting its potential as a valuable tool to support pathologists in diagnostic activities.

RGSB-UNet: Hybrid Deep Learning Framework for Tumour Segmentation in Digital Pathology Images

Colorectal cancer (CRC) is a prevalent gastrointestinal tumour with high incidence and mortality rates. Early screening for CRC can improve cure rates and reduce mortality. Recently, deep convolution neural network (CNN)-based pathological image diagnosis has been intensively studied to meet the challenge of time-consuming and labour-intense manual analysis of high-resolution whole slide images (WSIs). Despite the achievements made, deep CNN-based methods still suffer from some limitations, and the fundamental problem is that they cannot capture global features. To address this issue, we propose a hybrid deep learning framework (RGSB-UNet) for automatic tumour segmentation in WSIs. The framework adopts a UNet architecture that consists of the newly-designed residual ghost block with switchable normalization (RGS) and the bottleneck transformer (BoT) for downsampling to extract refined features, and the transposed convolution and 1 × 1 convolution with ReLU for upsampling to restore the feature map resolution to that of the original image. The proposed framework combines the advantages of the spatial-local correlation of CNNs and the long-distance feature dependencies of BoT, ensuring its capacity of extracting more refined features and robustness to varying batch sizes. Additionally, we consider a class-wise dice loss (CDL) function to train the segmentation network. The proposed network achieves state-of-the-art segmentation performance under small batch sizes. Experimental results on DigestPath2019 and GlaS datasets demonstrate that our proposed model produces superior evaluation scores and state-of-the-art segmentation results.

Investigation of the therapeutic role of native plant compounds against colorectal cancer based on system biology and virtual screening

This study investigated the anticancer effects of compounds extracted from native plants on colon cancer following drug-target-network analysis and molecular docking. Based on the ChEBI database, compounds were identified in medicinal plants and weeds in the Chaharmahal and Bakhtiari provinces of Iran. A drug-target network was constructed based on candidate colon cancer protein targets and selective compounds. Network pharmacology analysis was conducted against the identified compounds and subjected to molecular docking studies. Based on molecular dynamics simulations, the most efficient compounds were evaluated for their anticancer effects. Our study suggests that TREM1, MAPK1, MAPK8, CTSB, MIF, and DPP4 proteins may be targeted by compounds in medicinal plants for their anti-cancer effects. Multiorthoquinone, Liquiritin, Isoliquiritin, Hispaglabridin A, Gibberellin A98, Cyclomulberrin, Cyclomorusin A, and Cudraflavone B are effective anticancer compounds found in targeted medicinal plants and play an important role in the regulation of important pathways in colon cancer. Compounds that inhibit MIF, CTSB, and MAPK8-16 appear to be more effective. Additional in vitro and in vivo experiments will be helpful in validating and optimizing the findings of this study.

The Expression of Stem Cell Marker LGR5 and Its Coexpression with Β-Catenin in Sporadic Colorectal Carcinoma and Adenoma: A Comparative Immunohistochemical Study

Background: LGR5 is one of the most important stem cell markers for colorectal cancer (CRC), as it potentiates Wnt/Β-catenin signaling. The well-characterized deregulation of Wnt/Β-catenin signaling that occurs during adenoma/carcinoma sequence in CRC renders LGR5 a hopeful therapeutic target. We assessed the immunohistochemical expression of LGR5 and Β-catenin in normal colonic and tumorous lesions with a clinicopathological correlation. Methods: Tissue blocks and clinical data of 50 selected cases were included: 8 from normal mucosa, 12 cases of adenoma, and 30 cases of CRC, where sections were cut and re-examined and the immunohistochemical technique was conducted using anti-LGR5 and anti-Β-catenin to measure the staining density. Results: There was no expression of LGR5 in normal mucosa compared to samples of adenoma and CRC samples. The association analysis showed that CRC specimens were more likely to have strong LGR5 and Β-catenin expressions than the other two groups (p = 0.048 and p < 0.001, respectively). Specimens with high-grade dysplastic adenoma were more likely to express moderate-to-strong expression of LGR5 and Β-catenin (p = 0.013 and p = 0.036, respectively). In contrast, there were no statistically significant associations between LGR5 and Β-catenin expression with grade and stage. Conclusion: These results suggest and support the possible role of LGR5 as a potential marker of cancer stem cells in sporadic colorectal carcinogenesis in addition to a prognostic value for LGR5 and Β-catenin in adenomatous lesions according to immunohistochemical expression density. A potential therapeutic role of LGR5 in CRC is suggested for future studies based on its role in pathogenesis.

Overexpression of miRNA 26a and 26b with MMP-9 are valuable diagnostic biomarkers for colorectal cancer patients

Background: The key role of miRNA expression in incidence and progression of colorectal cancer (CLC) have been developed over the last decade. Materials & methods: A total of 153 subjects were enrolled into two phases: 14 selected miRNAs were first evaluated in 50 subjects, then miR-26a and miR-26b relative expression were further evaluated in 103 subjects and their target protein MMP-9 was measured. Results: miR-26a and -26b showed highly significant overexpression. Both miR-26a and -26b (p < 0.001) had high diagnostic efficacy for CRC. There was a significant increase in serum MMP-9 protein in CRC patients with positive correlation with miR-26a and -26b expression levels (p < 0.001). Conclusion: miRNA 26a and 26b with MMP-9 can be used as diagnostic biomarker for CRC patients.

Isolation and Characterization of Extracellular Vesicles in Human Bowel Lavage Fluid

Colorectal cancer (CRC) is the third most common cancer worldwide and is detected in late stages because of a lack of early and specific biomarkers. Tumors can release extracellular vesicles (EVs), which participate in different functions, such as carrying nucleic acids to target cells; promoting angiogenesis, invasion, and metastasis; and preparing an adequate tumor microenvironment. Finally, bowel lavage fluid (BLF) is a rarely used sample that is obtained during colonoscopy. It presents low variability and protein degradation, is easy to handle, and is representative of EVs from tumor cells due to proximity of the sample collection. This sample has potential as a research tool and possible biomarker source for CRC prognosis and monitoring. In this study, EVs were isolated from human BLF by ultracentrifugation, then characterized by transmission electron microscopy and atomic force microscopy. EV concentration was determined by nanoparticle tracking analysis, and tetraspanins were determined by Western blot, confirming correct EV isolation. RNA, DNA, and proteins were isolated from these EVs; RNA was used in real-time PCR, and proteins were used in an immunoblotting analysis, indicating that EV cargo is optimal for use and study. These results indicate that EVs from BLF can be a useful tool for CRC study and could be a source of biomarkers for the diagnosis and monitoring of CRC.

NSPA: characterizing the disease association of multiple genetic interactions at single-subject resolution

Motivation

The interaction between genetic variables is one of the major barriers to characterizing the genetic architecture of complex traits. To consider epistasis, network science approaches are increasingly being used in research to elucidate the genetic architecture of complex diseases. Network science approaches associate genetic variables' disease susceptibility to their topological importance in the network. However, this network only represents genetic interactions and does not describe how these interactions attribute to disease association at the subject-scale. We propose the Network-based Subject Portrait Approach (NSPA) and an accompanying feature transformation method to determine the collective risk impact of multiple genetic interactions for each subject.

Results

The feature transformation method converts genetic variants of subjects into new values that capture how genetic variables interact with others to attribute to a subject's disease association. We apply this approach to synthetic and genetic datasets and learn that (1) the disease association can be captured using multiple disjoint sets of genetic interactions and (2) the feature transformation method based on NSPA improves predictive performance comparing with using the original genetic variables. Our findings confirm the role of genetic interaction in complex disease and provide a novel approach for gene-disease association studies to identify genetic architecture in the context of epistasis.

Availability and implementation

The codes of NSPA are now available in: https://github.com/MIB-Lab/Network-based-Subject-Portrait-Approach.

Contact

ting.hu@queensu.ca.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Deviating Alternative Splicing as a Molecular Subtype of Microsatellite Stable Colorectal Cancer

PURPOSE

Colorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide. Biomarkers to aid in prognostication and treatment decisions are in high demand, and to facilitate their development, a better understanding of the underlying biology of the highly heterogeneous disease is needed.

METHODS

A genome-scale alternative splicing (AS) analysis using RNA-sequencing data from primary microsatellite stable (MSS) CRCs from 127 patients was performed. Splice variant-specific expression levels of individual cancer samples were compared with the total set of samples, and a metric for a tumor sample's global amount of deviating AS was developed. This metric varied considerably across the cohort and ranged from 6 to 282 deviating AS events per tumor sample. A threshold of 45 or more deviating events was set to distinguish cancers with high (n = 44) and low (n = 83) levels of deviating AS.

RESULTS

Patients with high amounts of AS deviations had significantly shorter time to relapse compared with patients with fewer deviations (P = .04). Furthermore, differential gene expression analysis revealed nine known cancer-critical genes that are significantly upregulated in samples with high amounts of deviating AS. Validation of the results in an independent cohort of MSS CRCs showed the same tendency toward shorter progression-free survival among the high-deviation group. In both cohorts, enrichment for growth factors was identified among upregulated genes associated with this phenotype.

CONCLUSION

There is a large variation in the amount of deviating AS among MSS CRCs, and we provide evidence that those with high amounts of deviations represent different cancer biology.

Colon Adenocarcinoma Quantitative Proteomics Reveals Dysregulation in Key Cancer Signaling Pathways and a Candidate Protein Marker Panel

Colorectal cancer (CRC) is reportedly the second leading cause of cancer death worldwide. By the end of the decade, there will likely be more than one million fatalities worldwide from this cancer, with an estimated 2.2 million additional cases. We need new ways of thinking about cancer research. One approach is to deploy systems science using quantitative proteomics to obtain postgenomic and functional insights into cancer. The present study compares the tissue proteome of CRC (n = 10) with the matched peritumoral controls (n = 10) in samples obtained from the Indian subcontinent. When compared with the controls, a list of 22 substantially altered protein candidates was identified, which were associated with the growth, survival, and metastasis of the tumor. A list of the unique peptides from top significant proteins, including olfactomedin-4, alanyl aminopeptidase, and grancalcin was further validated using a parallel reaction monitoring-based targeted proteomics approach. In addition, biological pathway analysis showed perturbation in key biological processes, including dysregulation in purine metabolism, MYC targets in cancer, DNA repair, and replication, and leukocyte transendothelial migration, among others. The protein panel reported herein is also shown to be dysregulated in CRC and warrants further research toward understanding pathobiology, diagnostics, and therapeutics development in CRC.

Role of artificial intelligence in risk prediction, prognostication, and therapy response assessment in colorectal cancer: current state and future directions

Artificial Intelligence (AI) is a branch of computer science that utilizes optimization, probabilistic and statistical approaches to analyze and make predictions based on a vast amount of data. In recent years, AI has revolutionized the field of oncology and spearheaded novel approaches in the management of various cancers, including colorectal cancer (CRC). Notably, the applications of AI to diagnose, prognosticate, and predict response to therapy in CRC, is gaining traction and proving to be promising. There have also been several advancements in AI technologies to help predict metastases in CRC and in Computer-Aided Detection (CAD) Systems to improve miss rates for colorectal neoplasia. This article provides a comprehensive review of the role of AI in predicting risk, prognosis, and response to therapies among patients with CRC.

Intestinal bacteria and colorectal cancer: etiology and treatment

The etiology of colorectal cancer (CRC) is influenced by bacterial communities that colonize the gastrointestinal tract. These microorganisms derive essential nutrients from indigestible dietary or host-derived compounds and activate molecular signaling pathways necessary for normal tissue and immune function. Associative and mechanistic studies have identified bacterial species whose presence may increase CRC risk, including notable examples such as Fusobacterium nucleatum, Enterotoxigenic Bacteroides fragilis, and pks+ E. coli. In recent years this work has expanded in scope to include aspects of host mutational status, intra-tumoral microbial heterogeneity, transient infection, and the cumulative influence of multiple carcinogenic bacteria after sequential or co-colonization. In this review, we will provide an updated overview of how host-bacteria interactions influence CRC development, how this knowledge may be utilized to diagnose or prevent CRC, and how the gut microbiome influences CRC treatment efficacy.

Microbiome Profiling from Fecal Immunochemical Test Reveals Microbial Signatures with Potential for Colorectal Cancer Screening

Colorectal cancer (CRC) is the third most common cancer and the second leading cause of cancer deaths worldwide. Early diagnosis of CRC, which saves lives and enables better outcomes, is generally implemented through a two-step population screening approach based on the use of Fecal Immunochemical Test (FIT) followed by colonoscopy if the test is positive. However, the FIT step has a high false positive rate, and there is a need for new predictive biomarkers to better prioritize cases for colonoscopy. Here we used 16S rRNA metabarcoding from FIT positive samples to uncover microbial taxa, taxon co-occurrence and metabolic features significantly associated with different colonoscopy outcomes, underscoring a predictive potential and revealing changes along the path from healthy tissue to carcinoma. Finally, we used machine learning to develop a two-phase classifier which reduces the current false positive rate while maximizing the inclusion of CRC and clinically relevant samples.

CAM-Xenograft Model Provides Preclinical Evidence for the Applicability of [68Ga]Ga-Pentixafor in CRC Imaging

Colorectal cancer is one of the leading causes of cancer-related deaths worldwide. Increased expression of CXCR4 has been associated with liver metastasis, disease progression, and shortened survival. Using in vitro cell binding studies and the in ovo model, we aimed to investigate the potential of [⁶⁸Ga]Ga-Pentixafor, a radiotracer specifically targeting human CXCR4, for CRC imaging. Specific membrane binding and internalisation of [⁶⁸Ga]Ga-Pentixafor was shown for HT29 cells, but not for HCT116 cells. Accordingly, [⁶⁸Ga]Ga-Pentixafor accumulated specifically in CAM-xenografts derived from HT29 cells, but not in HCT116 xenografts, as determined by µPET/MRI. The CAM-grown xenografts were histologically characterised, demonstrating vascularisation of the graft, preserved expression of human CXCR4, and viability of the tumour cells within the grafts. In vivo viability was further confirmed by µPET/MRI measurements using 2-[¹⁸F]FDG as a surrogate for glucose metabolism. [⁶⁸Ga]Ga-Pentixafor µPET/MRI scans showed distinct radiotracer accumulation in the chick embryonal heart, liver, and kidneys, whereas 2-[¹⁸F]FDG uptake was predominantly found in the kidneys and joints of the chick embryos. Our findings suggest that [⁶⁸Ga]Ga-Pentixafor is an interesting novel radiotracer for CRC imaging that is worth further investigation. Moreover, this study further supports the suitability of the CAM-xenograft model for the initial preclinical evaluation of targeted radiopharmaceuticals.

The Clinical Value of Blood miR-654-5p, miR-126, miR-10b, and miR-144 in the Diagnosis of Colorectal Cancer

Colorectal cancer (CRC) is the third cause of cancer-related death and the fourth most frequently diagnosed cancer across the globe. The objective of this study is to obtain novel and effective diagnostic markers to enrich CRC diagnosis methods. Herein, exosomal miRNA expression data of CRC and normal blood were subjected to XGBoost algorithm, and 5 miRNAs related to CRC diagnosis were primarily confirmed. Then multilayer perceptron (MLP) classifiers were constructed based on different subsets. Via integrated feature selection (IFS), we noticed that the MLP classifier constructed by the first four miRNAs (miR-654-5p, miR-126, miR-10b, and miR-144) had the highest Matthews correlation coefficient (MCC). Subsequently, principal component analysis (PCA) for dimensionality reduction was performed on samples based on the miR-654-5p, miR-126, miR-10b, and miR-144 expression data. The signature based on these four feature miRNAs, as the analysis indicated, could effectively distinguish CRC samples from normal samples. Further, we extracted the exosomes from clinical blood samples and applied qRT-PCR analysis, which revealed that the expression of these four feature miRNAs was in the trend of that in the test set. Collectively, these four feature miRNAs might be tumor biomarkers in the serum, and our study offers innovative thinking on early-stage CRC diagnosis.

Early onset colorectal cancer: Challenges across the cancer care continuum

Early Onset Colorectal cancer (EOCRC) incidence is increasing at an alarming pace. An increase of 90% in colon cancer and 124% in rectal cancer is expected by 2030. Patients with EOCRC are not receiving additional attention compared to older patients despite having a unique molecular pattern, majority of cases are sporadic, and related short- and long-term treatment and disease complications. The current management and screening guidelines have been constructed from studies on late onset CRC. Plethora of studies are ongoing to understand this disease entity in order to construct a tailored prevention, detection and management plans. While waiting for a better understanding of the disease, efforts should be directed toward improving the quality of care across the cancer continuum. Here we aim to address the challenges faced by EOCRC patients across the cancer continuum. This will facilitate directing future efforts and research toward construction of a personalized and precise guidelines.

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Studies showed that Ealy onset colorectal cancer is caused by an accumulative risk exposure.

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Indirect cost of premature death and reduced productivity due to EOCRC exceeds direct costs.

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Patients, system and physicians related diagnosis delays should be improved.

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Young cancer patients have unique survivorship concerns different from old patients.

The Prediction of Peritoneal Carcinomatosis in Patients with Colorectal Cancer Using Machine Learning

The incidence of colon, rectal, and colorectal cancer is very high, and diagnosis is often made in the advanced stages of the disease. In cases where peritoneal carcinomatosis is limited, patients can benefit from newer treatment options if the disease is promptly identified, and they are referred to specialized centers. Therefore, an essential diagnostic benefit would be identifying those factors that could lead to early diagnosis. A retrospective study was performed using patient data gathered from 2010 to 2020. The collected data were represented by routine blood tests subjected to stringent inclusion and exclusion criteria. In order to determine the presence or absence of peritoneal carcinomatosis in colorectal cancer patients, three types of machine learning approaches were applied: a neuro-evolutive methodology based on artificial neural network (ANN), support vector machines (SVM), and random forests (RF), all combined with differential evolution (DE). The optimizer (DE in our case) determined the internal and structural parameters that defined the ANN, SVM, and RF in their optimal form. The RF strategy obtained the best accuracy in the testing phase (0.75). Using this RF model, a sensitivity analysis was applied to determine the influence of each parameter on the presence or absence of peritoneal carcinomatosis.