Biomarkers in colorectal cancer: Current clinical utility and future perspectives

LncRNA DANCR-V1 is a novel regulator of Wnt/β-catenin and TGF-β1/SMAD signaling pathways in colorectal cancer: an in vitro and in silico study

BACKGROUND

DANCR is an oncogenic lncRNA associated with advanced colorectal cancer, one of the most common malignancies worldwide. This lncRNA has a new variant, DANCR-V1, whose function is not yet understood. In this study, we aimed to evaluate the expression pattern of DANCR-V1 and its regulatory mechanism in colorectal cancer.

METHOD AND RESULT

Bioinformatics analysis and RT-qPCR showed that DANCR-V1 expression was higher in colorectal cancer tissues than in normal pairs obtained from microarray data and 20 samples, respectively. LncRNA subcellular localization and hsa-miR-222 binding sites were predicted using bioinformatics tools. Dual luciferase assays confirmed that miR-222-mediated downregulation of DANCR-V1 through its targeting, and RT-qPCR showed that overexpression of miR-222 decreased the level of DANCR-V1. Functionally, Wnt/β-catenin and TGF-β1/SMAD-related genes changed under DANCR-V1 overexpression in the SW480 cell line, while their expression was reversed following miR-222 overexpression. Finally, at the cellular level, overexpression of DANCR-V1 elevated the proliferation and migration rates of SW480 cells, as determined using flow cytometry, western blotting and scratch assays.

CONCLUSION

Our data suggest that DANCR-V1 is a novel transcript variant that has crucial crosstalk with miR-222 via negative feedback and plays a critical role in colorectal cancer progression through Wnt/β-catenin and TGF-β1/SMAD signaling modulation.

Computational pathology: A survey review and the way forward

Computational Pathology (CPath) is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath. For updated information on this survey review paper and accessing to the original model cards repository, please refer to GitHub. Updated version of this draft can also be found from arXiv.

Docosahexaenoic acid enhances the treatment efficacy for castration-resistant prostate cancer by inhibiting autophagy through Atg4B inhibition

Autophagy induction in cancer is involved in cancer progression and the acquisition of resistance to anticancer agents. Therefore, autophagy is considered a potential therapeutic target in cancer therapy. In this study, we found that long-chain fatty acids (LCFAs) have inhibitory effects on Atg4B, which is essential for autophagosome formation, through screening based on the pharmacophore of 21f, a recently developed Atg4B inhibitor. Among these fatty acids, docosahexaenoic acid (DHA), a polyunsaturated fatty acid, exhibited the most potent Atg4B inhibitory activity. DHA inhibited autophagy induced by androgen receptor signaling inhibitors (ARSI) in LNCaP and 22Rv1 prostate cancer cells and significantly increased apoptotic cell death. Furthermore, we investigated the effect of DHA on resistance to ARSI by establishing darolutamide-resistant prostate cancer 22Rv1 (22Rv1/Dar) cells, which had developed resistance to darolutamide, a novel ARSI. At baseline, 22Rv1/Dar cells showed a higher autophagy level than parental 22Rv1 cells. DHA significantly suppressed Dar-induced autophagy and sensitized 22Rv1/Dar cells by inducing apoptotic cell death through mitochondrial dysfunction. These results suggest that DHA supplementation may improve prostate cancer therapy with ARSI.

DNA methylation analysis of the SDC2, SEPT9 and VIM genes in fecal DNA for colorectal cancer diagnosis

BACKGROUND

Colorectal cancer is one of the most common cancers worldwide. DNA methylation sites may serve as a new gene signature for colorectal cancer diagnosis. The search for representative DNA methylation sites is urgently needed. This study aimed to systematically identify a methylation gene panel for colorectal cancer diagnosis via tissue and fecal samples.

METHODS

A total of 181 fecal and 50 tumor tissue samples were collected. They were obtained from 83 colorectal cancer patients and 98 healthy subjects. These samples were evaluated for DNA methylation of 9 target genes via quantitative bisulfite next-generation sequencing. We employed the rank-sum test to screen the colorectal cancer-specific methylation sites in the tissue and fecal cohorts. A data model was subsequently constructed and validated via the dedicated validation dataset.

RESULTS

Compared with the fecal and negative control samples, the colorectal cancer tissue samples presented significantly higher methylation rates for all the selected gene sites. The methylation rates of the tissue and preoperative fecal samples showed the same high and low rates at the same sites. After screening, a panel of 29 loci in the SDC2, SEPT9, and VIM genes proved to be reliable biomarkers for colorectal cancer diagnosis in fecal samples. Logistic regression models were then constructed and validated using this panel. The sensitivity of the model was 91.43% (95% CI = [89.69, 93.17]), the specificity was 100% (95% CI = [100,100]), and the AUC value is 99.31% (95% CI = [99,99.62]). The diagnostic accuracy of the model for stage I and stage II colorectal cancer was 100% (11/11) and 91.3% (21/23), respectively. Overall, this study confirms that the gene locus panel and the model can be used to diagnose colorectal cancer effectively through feces.

CONCLUSIONS

Our study identified a set of key methylation sites for colorectal cancer diagnosis from fecal samples, highlighting the importance of using tissue and fecal samples to accurately assess DNA methylation levels to screen for methylation sites, and developing an effective diagnostic model for colorectal cancer.

Clinicopathological and molecular differences between stage IV screen-detected and interval colorectal cancers in the Flemish screening program

Introduction

Interval cancer (IC) is an important quality indicator in colorectal cancer (CRC) screening. Previously, we found that fecal immunochemical test (FIT) ICs are more common in women, older age, right-sided tumors, and advanced stage. Here, we extended our existing stage IV patient cohort with clinicopathological and molecular characteristics, to identify factors associated with FIT-IC.

Methods

Logistic regression models were fit to identify variables associated with the odds of having a stage IV FIT-IC. Multivariate models were corrected for gender, age, and location.

Results

A total of 292 screen-detected (SD) CRCs and 215 FIT-IC CRCs were included. FIT-IC CRC had 5 fold higher odds to be a neuroendocrine (NET) tumor and 2.5 fold higher odds to have lymphovascular invasion. Interestingly, some variables lost significance upon accounting for location. Thus, tumor location is a critical covariate that should always be included when evaluating factors related to FIT-IC.

Conclusions

We identified NETs and lymphovascular invasion as factors associated with increased odds of having a stage IV FIT-IC. Moreover, we highlight the importance of tumor location as a covariate in evaluating FIT-IC related factors. More research across all stages is needed to clarify how these insights might help to optimize the Flemish CRC screening program.

Novel regimens and treatment strategies in neoadjuvant therapy for colorectal cancer: A systematic review

Objective

The objective of this systematic review was to describe novel regimens and treatment strategies in neoadjuvant therapy for colorectal cancer (CRC). The aim was to summarize the current advancements in neoadjuvant chemotherapy (NACT) for CRC, including the use of cytotoxic drugs, targeted treatments, and immunotherapy. The analysis aimed to provide insights into the potential benefits and drawbacks of these novel approaches and highlight the need for further research to optimize NACT use in CRC and improve patient outcomes.

Methods

From October 20, 2023, to December 10, 2023, a comprehensive literature search was conducted across multiple databases, including PubMed, Ovid, Web of Science, the Cochrane Library, Cumulative Index to Nursing and Allied Health Literature, Embase, and Scopus. Studies addressing the use of and treatment strategies for CRC and neoadjuvant therapies were included. Screening was conducted in two steps, initially by title and abstract and then by full-text articles. English-language articles were considered, while preprints, non-English publications, and articles published as grey literature were excluded from the study. A total of 85 studies were selected for further analysis after screening and filtering.

Results

After filtering out duplicates and items that were irrelevant to our research query from the initial database search's 510 results, 397 unique articles were found. Eighty-five studies were chosen for additional analysis after the articles underwent two rounds of screening.

Conclusion

The review concluded that neoadjuvant therapy for CRC has evolved beyond conventional approaches and holds promise for improving patient outcomes. Future prospects for advancing neoadjuvant approaches are promising, with ongoing clinical trials investigating the refinement of strategies, identification of predictive biomarkers, and optimization of patient selection. The adoption of novel regimens, precision medicine, and immunotherapy offers opportunities to redefine treatment paradigms and enhance patient care in CRC.

Serum Uric Acid Level May Be a Predictive Factor for BRAF V600E Mutation in Older Patients with Metastatic Colorectal Cancer: An Exploratory Analysis

INTRODUCTION

This study aimed to show the relationship between the serum uric acid level measured at diagnosis and the BRAF mutation status in the primary tumor tissue in patients with metastatic colorectal cancer.

METHODS

In this retrospective cross-sectional study, 264 patients (64% male) whose serum uric acid level was measured at the time of diagnosis and whose BRAF mutation status in the primary tumor was determined were included.

RESULTS

The BRAF mutation rate was 14% (n = 37). The median serum uric acid levels of all patients were 6.9 mg/dL (25%, 75% percentile range 3.7, 8.2). The serum uric acid level cut-off value was 6.6 mg/dL. Sensitivity and specificity for BRAF mutated patients were 84% and 27%, respectively. These rates were calculated as 85% and 70% in BRAF-mutated patients aged 65 and over. There was a significant correlation between BRAF mutation and high serum uric acid level, female gender, tumor located in the ascending colon, and multiple metastatic sites. The independent factors affecting BRAF mutation were age 65 and over, tumor in the ascending colon, and high serum uric acid level.

CONCLUSION

As a result, we concluded that high serum uric acid level measured during diagnosis in metastatic colorectal cancer is an accessible and economical biomarker that can predict BRAF mutation in patients aged 65 and over.

Association of Serum Proteases and Acute Phase Factors Levels with Survival Outcomes in Patients with Colorectal Cancer

Colorectal cancer (CRC) represents a substantial burden on global healthcare, contributing to significant morbidity and mortality worldwide. Despite advances in screening methodologies, its incidence remains high, necessitating continued efforts in early detection and treatment. Neoplastic invasion and metastasis are primary determinants of CRC lethality, emphasizing the urgency of understanding underlying mechanisms to develop effective therapeutic strategies. This study aimed to explore the potential of serum biomarkers in predicting survival outcomes in CRC patients, with a focus on cathepsin B (CB), leukocytic elastase (LE), total sialic acid (TSA), lipid-associated sialic acid (LASA), antitrypsin activity (ATA), C-reactive protein (CRP), and cystatin C (CC). We recruited 185 CRC patients and 35 healthy controls, assessing demographic variables, tumor characteristics, and 7 serum biomarker levels, including (1) CB, (2) LE, (3) TSA, (4) LASA, (5) ATA, (6) CRP, and (7) CC. Statistical analyses included ANOVA with Tukey's post hoc tests and MANOVA for continuous variables. Student's t-test was used for dependent samples, while non-parametric tests like Mann-Whitney U and Wilcoxon signed-rank tests were applied for variables deviating from the normal distribution. Categorical variables were assessed using chi-square and Kruskal-Wallis tests. Spearman's rank correlation coefficient was utilized to examine variable correlations. Survival analysis employed the Kaplan-Meier method with a log-rank test for comparing survival times between groups. Significant associations were observed between CB (p = 0.04), LE (p = 0.01), and TSA (p = 0.008) levels and survival outcomes in CRC patients. Dukes' classification stages also showed a significant correlation with survival (p = 0.001). However, no significant associations were found for LASA, ATA, CRP, and CC. Multivariate analysis of LE, TSA, and ATA demonstrated a notable correlation with survival (p = 0.041), notwithstanding ATA's lack of significance in univariate analysis (p = 0.13). CB, LE, and TSA emerged as promising diagnostic markers with prognostic value in CRC, potentially aiding in early diagnosis and treatment planning. Further research is needed to validate these findings and explore additional prognostic indicators.

Comprehensive analysis of gene mutations and mismatch repair in Chinese colorectal cancer patients

BACKGROUND

RAS, BRAF, and mismatch repair (MMR)/microsatellite instability (MSI) are crucial biomarkers recommended by clinical practice guidelines for colorectal cancer (CRC). However, their characteristics and influencing factors in Chinese patients have not been thoroughly described.

AIM

To analyze the clinicopathological features of KRAS, NRAS, BRAF, and PIK3CA mutations and the DNA MMR status in CRC.

METHODS

We enrolled 2271 Chinese CRC patients at the China-Japan Friendship Hospital. MMR proteins were tested using immunohistochemical analysis, and the KRAS/NRAS/BRAF/PIK3CA mutations were determined using quantitative polymerase chain reaction. Microsatellite status was determined using an MSI detection kit. Statistical analyses were conducted using SPSS software and logistic regression.

RESULTS

The KRAS, NRAS, BRAF, and PIK3CA mutations were detected in 44.6%, 3.4%, 3.7%, and 3.9% of CRC patients, respectively. KRAS mutations were more likely to occur in patients with moderate-to-high differentiation. BRAF mutations were more likely to occur in patients with right-sided CRC, poorly differentiated, or no perineural invasion. Deficient MMR (dMMR) was detected in 7.9% of all patients and 16.8% of those with mucinous adenocarcinomas. KRAS, NRAS, BRAF, and PIK3CA mutations were detected in 29.6%, 1.1%, 8.1%, and 22.3% of patients with dMMR, respectively. The dMMR was more likely to occur in patients with a family history of CRC, aged < 50 years, right-sided CRC, poorly differentiated histology, no perineural invasion, and with carcinoma in situ, stage I, or stage II tumors.

CONCLUSION

This study analyzed the molecular profiles of KRAS, NRAS, BRAF, PIK3CA, and MMR/MSI in CRC, identifying key influencing factors, with implications for clinical management of CRC.

Comprehensive analysis of gene mutations and mismatch repair in Chinese colorectal cancer patients

BACKGROUND

RAS, BRAF, and mismatch repair (MMR)/microsatellite instability (MSI) are crucial biomarkers recommended by clinical practice guidelines for colorectal cancer (CRC). However, their characteristics and influencing factors in Chinese patients have not been thoroughly described.

AIM

To analyze the clinicopathological features of KRAS, NRAS, BRAF, and PIK3CA mutations and the DNA MMR status in CRC.

METHODS

We enrolled 2271 Chinese CRC patients at the China-Japan Friendship Hospital. MMR proteins were tested using immunohistochemical analysis, and the KRAS/NRAS/BRAF/PIK3CA mutations were determined using quantitative polymerase chain reaction. Microsatellite status was determined using an MSI detection kit. Statistical analyses were conducted using SPSS software and logistic regression.

RESULTS

The KRAS, NRAS, BRAF, and PIK3CA mutations were detected in 44.6%, 3.4%, 3.7%, and 3.9% of CRC patients, respectively. KRAS mutations were more likely to occur in patients with moderate-to-high differentiation. BRAF mutations were more likely to occur in patients with right-sided CRC, poorly differentiated, or no perineural invasion. Deficient MMR (dMMR) was detected in 7.9% of all patients and 16.8% of those with mucinous adenocarcinomas. KRAS, NRAS, BRAF, and PIK3CA mutations were detected in 29.6%, 1.1%, 8.1%, and 22.3% of patients with dMMR, respectively. The dMMR was more likely to occur in patients with a family history of CRC, aged < 50 years, right-sided CRC, poorly differentiated histology, no perineural invasion, and with carcinoma in situ, stage I, or stage II tumors.

CONCLUSION

This study analyzed the molecular profiles of KRAS, NRAS, BRAF, PIK3CA, and MMR/MSI in CRC, identifying key influencing factors, with implications for clinical management of CRC.

Advances in Precision Medicine Approaches for Colorectal Cancer: From Molecular Profiling to Targeted Therapies

Precision medicine is transforming colorectal cancer treatment through the integration of advanced technologies and biomarkers, enhancing personalized and effective disease management. Identification of key driver mutations and molecular profiling have deepened our comprehension of the genetic alterations in colorectal cancer, facilitating targeted therapy and immunotherapy selection. Biomarkers such as microsatellite instability (MSI) and DNA mismatch repair deficiency (dMMR) guide treatment decisions, opening avenues for immunotherapy. Emerging technologies such as liquid biopsies, artificial intelligence, and machine learning promise to revolutionize early detection, monitoring, and treatment selection in precision medicine. Despite these advancements, ethical and regulatory challenges, including equitable access and data privacy, emphasize the importance of responsible implementation. The dynamic nature of colorectal cancer, with its tumor heterogeneity and clonal evolution, underscores the necessity for adaptive and personalized treatment strategies. The future of precision medicine in colorectal cancer lies in its potential to enhance patient care, clinical outcomes, and our understanding of this intricate disease, marked by ongoing evolution in the field. The current reviews focus on providing in-depth knowledge on the various and diverse approaches utilized for precision medicine against colorectal cancer, at both molecular and biochemical levels.

Singlet oxygen-based photoelectrochemical detection of single-point mutations in the KRAS oncogene

Single nucleotide point mutations in the KRAS oncogene occur frequently in human cancers, rendering them intriguing targets for diagnosis, early detection and personalized treatment. Current detection methods are based on polymerase chain reaction, sometimes combined with next-generation sequencing, which can be expensive, complex and have limited availability. Here, we propose a novel singlet oxygen (1O2)-based photoelectrochemical detection methodology for single-point mutations, using KRAS mutations as a case study. This detection method combines the use of a sandwich assay, magnetic beads and robust chemical photosensitizers, that need only air and light to produce 1O2, to ensure high specificity and sensitivity. We demonstrate that hybridization of the sandwich hybrid at high temperatures enables discrimination between mutated and wild-type sequences with a detection rate of up to 93.9%. Additionally, the presence of background DNA sequences derived from human cell-line DNA, not containing the mutation of interest, did not result in a signal, highlighting the specificity of the methodology. A limit of detection as low as 112 pM (1.25 ng/mL) was achieved without employing any amplification techniques. The developed 1O2-based photoelectrochemical methodology exhibits unique features, including rapidity, ease of use, and affordability, highlighting its immense potential in the field of nucleic acid-based diagnostics.

Identification of prognostic models for glycosylation-related subtypes and tumor microenvironment infiltration characteristics in clear cell renal cell cancer

Background

One of the most fatal forms of cancer of the urinary system, renal cell carcinoma (RCC), significantly negatively impacts human health. Recent research reveals that abnormal glycosylation contributes to the growth and spread of tumors. However, there is no information on the function of genes related to glycosylation in RCC.

Methods

In this study, we created a technique that can be used to guide the choice of immunotherapy and chemotherapy regimens for RCC patients while predicting their survival prognosis. The Cancer Genome Atlas (TCGA) provided us with patient information, while the GeneCards database allowed us to collect genes involved in glycosylation. GSE29609 was used as external validation to assess the accuracy of prognostic models. The "ConsensusClusterPlus" program created molecular subtypes based on genes relevant to glycosylation discovered using differential expression analysis and univariate Cox analysis. We examined immune cell infiltration as measured by estimate, CIBERSORT, TIMER, and ssGSEA algorithms, Tumor Immune Dysfunction and Exclusion (TIDE) and exclusion of tumour stemness indices (TSIs) based on glycosylation-related molecular subtypes and risk profiles. Stratification, somatic mutation, nomogram creation, and chemotherapy response prediction were carried out based on risk factors.

Results

We built and verified 16 gene signatures associated with the prognosis of ccRCC patients, which are independent prognostic variables, and identified glycosylation-related genes by bioinformatics research. Cluster 2 is associated with lower human leukocyte antigen expression, worse overall survival, higher immunological checkpoints, and higher immune escape scores. In addition, cluster 2 had significantly better angiogenic activity, mesenchymal EMT, and stem ability scores. Higher immune checkpoint genes and human leukocyte antigens are associated with lower overall survival and a higher risk score. Higher estimated and immune scores, lesser tumor purity, lower mesenchymal EMT, and higher stem scores were all characteristics of the high-risk group. High amounts of tumor-infiltrating lymphocytes, a high mutation load, and a high copy number alteration frequency were present in the high-risk group.Discussion.According to our research, the 16-gene prognostic signature may be helpful in predicting prognosis and developing individualized treatments for patients with renal clear cell carcinoma, which may result in new personalized management options for these patients.

Exploring the Role of Non-synonymous and Deleterious Variants Identified in Colorectal Cancer: A Multi-dimensional Computational Scrutiny of Exomes

Introduction

Colorectal cancers are the world's third most commonly diagnosed type of cancer. Currently, there are several diagnostic and treatment options to combat it. However, a delay in detection of the disease is life-threatening. Additionally, a thorough analysis of the exomes of cancers reveals potential variation data that can be used for early disease prognosis.

Methods

By utilizing a comprehensive computational investigation, the present study aimed to reveal mutations that could potentially predispose to colorectal cancer. Ten colorectal cancer exomes were retrieved. Quality control assessments were performed using FastQC and MultiQC, gapped alignment to the human reference genome (hg19) using Bowtie2 and calling the germline variants using Haplotype caller in the GATK pipeline. The variants were filtered and annotated using SIFT and PolyPhen2 successfully categorized the mutations into synonymous, non-synonymous, start loss and stop gain mutations as well as marked them as possibly damaging, probably damaging and benign. This mutational profile helped in shortlisting frequently occurring mutations and associated genes, for which the downstream multi-dimensional expression analyses were carried out.

Results

Our work involved prioritizing the non-synonymous, deleterious SNPs since these polymorphisms bring about a functional alteration to the phenotype. The top variations associated with their genes with the highest frequency of occurrence included LGALS8, CTSB, RAD17, CPNE1, OPRM1, SEMA4D, MUC4, PDE4DIP, ELN and ADRA1A. An in-depth multi-dimensional downstream analysis of all these genes in terms of gene expression profiling and analysis and differential gene expression with regard to various cancer types revealed CTSB and CPNE1 as highly expressed and overregulated genes in colorectal cancer.

Conclusion

Our work provides insights into the various alterations that might possibly lead to colorectal cancer and suggests the possibility of utilizing the most important genes identified for wet-lab experimentation.

ARHGEF16 expression correlates with proliferation, migration and invasion of colon cancer cells

Background

Rho guanine nucleotide exchange factor 16 (ARHGEF16) is a newly discovered Rho-family guanine nucleotide exchange factor (GEF) involved in the activation of Rho-family GTPases. However, its roles in colon cancer cell proliferation, migration, and invasion remain unknown. This study analyzed the expression of ARHGEF16 in colon cancer and explored its biological effects on colon cancer cells, so as to find new therapeutic targets for the treatment of colon cancer.

Methods

The expression of ARHGEF16 in colon cancer tissues and cells was detected by bioinformatics analysis, western blot, and quantitative real time polymerase chain reaction (qRT-PCR) assays. The effects of overexpression and silencing of ARHGEF16 on the biological behavior of colon cancer cells were examined by Cell Counting Kit-8 (CCK-8), cell scratching and Transwell assays.

Results

The database showed that ARHGEF16 was highly expressed in colon cancer tissues. Validation with clinical fresh tissue specimens and colon cancer cell lines revealed that ARHGEF16 was highly expressed in both. The proliferation, growth, migration, and invasion ability of colon cancer cell lines increased significantly with the overexpression of ARHGEF16, while silencing ARHGEF16 showed the opposite effect.

Conclusions

The expression of ARHGEF16 is closely related to the migration and invasive ability of colon cancer cells, and overexpression of ARHGEF16 promotes the migration and invasion of colon cancer cells and correlates with the metastatic potential of colon cancer.

Current Research on Molecular Biomarkers for Colorectal Cancer in Stool Samples

Colorectal cancer (CRC) is one of the most diagnosed cancers worldwide, with a high incidence and mortality rate when diagnosed late. Currently, the methods used in healthcare to diagnose CRC are the fecal occult blood test, flexible sigmoidoscopy, and colonoscopy. However, the lack of sensitivity and specificity and low population adherence are driving the need to implement other technologies that can identify biomarkers that not only help with early CRC detection but allow for the selection of more personalized treatment options. In this regard, the implementation of omics technologies, which can screen large pools of biological molecules, coupled with molecular validation, stands out as a promising tool for the discovery of new biomarkers from biopsied tissues or body fluids. This review delves into the current state of the art in the identification of novel CRC biomarkers that can distinguish cancerous tissue, specifically from fecal samples, as this could be the least invasive approach.

Comparative Analysis of miRNA and EMT Markers in Metastatic Colorectal Cancer

Colorectal cancer (CRC) is the fourth most commonly diagnosed malignant condition in the world. Micro RNAs (miRNAs) as well as epithelial to mesenchymal transition (EMT) play an important role in the pathogenesis of CRC. We performed a comparative analysis of the expression of selected miRNA genes and EMT markers in bioptic samples from patients (n = 45) with primary CRC or metastatic (m)CRC to the regional lymph node using reverse transcription-quantitative PCR and IHC staining. Results: Out of all miRNA analyzed, the miR-17 expression was most significantly different and associated with lower risk of CRC spread to the lymph node. In addition, significant relationships were found between the tumor side localization and several miRNAs expressions (miR-9, miR-29b, miR-19a, miR-19b, miR-21, miR-106a, miR-20a and miR-17). In addition, of the examined EMT markers, only VEGFA expression correlated with tumor progression (tumor grade G2). In the examined set of patient samples and their matched healthy tissue, several specific molecular markers (miRNAs associated with EMT and tumor progression) were identified with a promising prognostic potential. Their further examination in larger patient cohorts is planned to validate the present data.

Wavelet scattering networks in deep learning for discovering protein markers in a cohort of Swedish rectal cancer patients

BACKGROUND

Cancer biomarkers play a pivotal role in the diagnosis, prognosis, and treatment response prediction of the disease. In this study, we analyzed the expression levels of RhoB and DNp73 proteins in rectal cancer, as captured in immunohistochemical images, to predict the 5-year survival time of two patient groups: one with preoperative radiotherapy and one without.

METHODS

The utilization of deep convolutional neural networks in medical research, particularly in clinical cancer studies, has been gaining substantial attention. This success primarily stems from their ability to extract intricate image features that prove invaluable in machine learning. Another innovative method for extracting features at multiple levels is the wavelet-scattering network. Our study combines the strengths of these two convolution-based approaches to robustly extract image features related to protein expression.

RESULTS

The efficacy of our approach was evaluated across various tissue types, including tumor, biopsy, metastasis, and adjacent normal tissue. Statistical assessments demonstrated exceptional performance across a range of metrics, including prediction accuracy, classification accuracy, precision, and the area under the receiver operating characteristic curve.

CONCLUSION

These results underscore the potential of dual convolutional learning to assist clinical researchers in the timely validation and discovery of cancer biomarkers.

Current advances in detecting genetic and epigenetic biomarkers of colorectal cancer

Colorectal carcinoma (CRC) is the third most common cancer in terms of diagnosis and the second in terms of mortality. Recent studies have shown that various proteins, extracellular vesicles (i.e., exosomes), specific genetic variants, gene transcripts, cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and altered epigenetic patterns, can be used to detect, and assess the prognosis of CRC. Over the last decade, a plethora of conventional methodologies (e.g., polymerase chain reaction [PCR], direct sequencing, enzyme-linked immunosorbent assay [ELISA], microarray, in situ hybridization) as well as advanced analytical methodologies (e.g., microfluidics, electrochemical biosensors, surface-enhanced Raman spectroscopy [SERS]) have been developed for analyzing genetic and epigenetic biomarkers using both optical and non-optical tools. Despite these methodologies, no gold standard detection method has yet been implemented that can analyze CRC with high specificity and sensitivity in an inexpensive, simple, and time-efficient manner. Moreover, until now, no study has critically reviewed the advantages and limitations of these methodologies. Here, an overview of the most used genetic and epigenetic biomarkers for CRC and their detection methods are discussed. Furthermore, a summary of the major biological, technical, and clinical challenges and advantages/limitations of existing techniques is also presented.

Integrated bioinformatics approach to unwind key genes and pathways involved in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is the fifth leading cause of death in India. Until now, the exact pathogenesis concerning CRC signaling pathways is largely unknown; however, the diseased condition is believed to deteriorate with lifestyle, aging, and inherited genetic disorders. Hence, the identification of hub genes and therapeutic targets is of great importance for disease monitoring.

OBJECTIVE

Identification of hub genes and targets for identification of candidate hub genes for CRC diagnosis and monitoring.

MATERIALS AND METHODS

The present study applied gene expression analysis by integrating two profile datasets (GSE20916 and GSE33113) from NCBI-GEO database to elucidate the potential key candidate genes and pathways in CRC. Differentially expressed genes (DEGs) between CRC (195 CRC tissues) and healthy control (46 normal mucosal tissue) were sorted using GEO2R tool. Further, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis were performed using Cluster Profiler in Rv. 3.6.1. Moreover, protein-protein interactions (PPI), module detection, and hub gene identification were accomplished and visualized through the Search Tool for the Retrieval of Interacting Genes, Molecular Complex Detection (MCODE) plug-in of Cytoscape v3.8.0. Further hub genes were imported into ToppGene webserver for pathway analysis and prognostic expression analysis was conducted using Gene Expression Profiling Interactive Analysis webserver.

RESULTS

A total of 2221 DEGs, including 1286 up-regulated and 935down-regulated genes mainly enriched in signaling pathways of NOD-like receptor, FoxO, AMPK signalling and leishmaniasis. Three key modules were detected from PPI network using MCODE. Besides, top 20 high prioritized hub genes were selected. Further, prognostic expression analysis revealed ten of the hub genes, namely IL1B, CD44, Glyceraldehyde-3-phosphate dehydrogenase (GAPDH, MMP9, CREB1, STAT1, vascular endothelial growth factor (VEGFA), CDC5 L, Ataxia-telangiectasia mutated (ATM + and CDH1 to be differently expressed in normal and cancer patients.

CONCLUSION

The present study proposed five novel therapeutic targets, i.e., ATM, GAPDH, CREB1, VEGFA, and CDH1 genes that might provide new insights into molecular oncogenesis of CRC.

Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study

Deep learning (DL) can accelerate the prediction of prognostic biomarkers from routine pathology slides in colorectal cancer (CRC). However, current approaches rely on convolutional neural networks (CNNs) and have mostly been validated on small patient cohorts. Here, we develop a new transformer-based pipeline for end-to-end biomarker prediction from pathology slides by combining a pre-trained transformer encoder with a transformer network for patch aggregation. Our transformer-based approach substantially improves the performance, generalizability, data efficiency, and interpretability as compared with current state-of-the-art algorithms. After training and evaluating on a large multicenter cohort of over 13,000 patients from 16 colorectal cancer cohorts, we achieve a sensitivity of 0.99 with a negative predictive value of over 0.99 for prediction of microsatellite instability (MSI) on surgical resection specimens. We demonstrate that resection specimen-only training reaches clinical-grade performance on endoscopic biopsy tissue, solving a long-standing diagnostic problem.