Biomarker For Colorectal Cancer Research Articles

Chondroitin polymerizing factor (CHPF) promotes the progression of colorectal cancer through ASB2-mediated ubiquitylation of SMAD9.

Chondroitin polymerizing factor (CHPF) has been reported to play a pivotal role in the progression of multiple cancers, however, the relationship between CHPF and colorectal cancer (CRC) progression has not been fully understood. The current study revealed that CHPF expression was upregulated in patients with CRC and correlated with an unfavorable prognosis. Also, CHPF knockdown effectively suppressed the viability and mobility of CRC cells and the growth of xenograft tumors. Additionally, SMAD9 was identified as a downstream target of CHPF. SMAD9 knockdown successfully abrogated the promotion of CHPF overexpression in CRC progression, indicating that CHPF regulated the development of CRC through SMAD9. Mechanistically, SMAD9 is ubiquitinated by ASB2, and the regulatory effect of CHPF on SMAD9 activity was exerted via its mediation of ASB2. Collectively, CHPF functioned as a promising prognostic biomarker and tumor-promoter of CRC by regulating the ASB2-mediated ubiquitination of SMAD9.

Exploring the Therapeutic Potential of Ginkgo biloba Polyphenols in Targeting Biomarkers of Colorectal Cancer: An In-silico Evaluation.

Colorectal cancer (CRC) is a major contributor to cancer-related deaths worldwide, driving the need for effective anticancer therapies with fewer side effects. The exploration of Ginkgo biloba, a natural source, offers a hopeful avenue for novel treatments targeting key colorectal biomarkers involved in CRC treatment. The aim of this study was to explore the binding affinity of natural molecules derived from G. biloba to essential biomarkers associated with CRC, including Kirsten rat sarcoma virus, neuroblastoma RAS mutations, serine/threonine-protein kinase B-Raf, phosphatidylinositol 3'-kinase, and deleted colorectal cancer, using molecular docking. The focus of this research was to evaluate how effectively these molecules bind to specified targets in order to identify potential inhibitors for the treatment of CRC. A total of 152 polyphenolic compounds from G. biloba were selected and subjected to molecular docking simulations to evaluate their interactions with CRC-related biomarkers. The docking results were analysed to identify ligands exhibiting strong affinities towards the targeted genes, suggesting potential inhibitory effects. Docking simulations unveiled the strong binding affinities between selected polyphenolic compounds derived from G. biloba and genes associated with CRC. The complex glycoside structures that are found in flavonols are of significant importance. These compounds, including derivatives with distinctive arrangements, exhibited promising docking scores, signifying substantial interactions with the targeted biomarkers. The study demonstrates the potential of G. biloba-derived molecules as effective anticancer agents for colorectal cancer. The identified ligands exhibit strong interactions with crucial CRC-related biomarkers, suggesting potential inhibition ability. Further in vitro and in vivo investigations are needed to validate and build upon these promising findings, advancing the development of novel and efficient CRC therapies.

Biomarkers in Colorectal Cancer: Actual and Future Perspectives

Biomarkers in colorectal cancer (CRC) are of great interest in the current literature due to improvements in techniques such as liquid biopsy and next-generation sequencing (NGS). However, screening methods vary globally, with multi-target stool DNA (mt-sDNA) predominantly used in the USA and, more recently, the Cologuard Plus; biomarkers such as the Galectins family and septins show promise in early detection. Gut microbiome assessments, such as Fusobacterium nucleatum, are under intense exploration. Diagnostic tests, such as circulating DNA analysis via NGS, exhibit effectiveness and are being increasingly adopted. Circulating tumor cells emerge as potential alternatives to traditional methods in terms of diagnosis and prognosis. Predictive biomarkers are well established in guidelines; nonetheless, with the aid of machine learning and artificial intelligence, these biomarkers may be improved. This review critically explores the actual dynamic landscape of CRC biomarkers and future, promising biomarkers involved in screening, diagnosis, and prognosis.

ALYREF recruits ELAVL1 to promote colorectal tumorigenesis via facilitating RNA m5C recognition and nuclear export

ALYREF can recognize 5-methylcytosine (m5C) decoration throughout RNAs to regulate RNA metabolism. However, its implications in cancer and precise regulatory mechanisms remain largely elusive. Here, we demonstrated that ALYREF supported colorectal cancer (CRC) growth and migration. Integrated analysis of ALYREF-RIP-Bis-seq and transcriptome profiles identified ribosomal protein S6 kinase B2 (RPS6KB2) and regulatory-associated protein of mTOR (RPTOR) as ALYREF’s possible downstream effectors. Mechanistically, ALYREF formed a complex with ELAV like RNA binding protein 1 (ELAVL1) to cooperatively promote m5C recognition and nuclear export of the two mRNAs. Moreover, ALYREF protein was highly expressed in tumor tissues of CRC patients, which predicted their poor prognosis. E2F transcription factor 6 (E2F6)-mediated transactivation gave a molecular insight into ALYREF overexpression. Collectively, ALYREF recruits ELAVL1 to collaboratively facilitate m5C recognition and nuclear export of RPS6KB2 and RPTOR transcripts for colorectal tumorigenesis, providing RNA m5C methylation as promising therapeutic targets and prognostic biomarkers for CRC.

PTPN11 is a potential biomarker for type 2 diabetes mellitus complicated with colorectal cancer

Epidemiological surveys have shown that the incidence of type 2 diabetes mellitus (T2DM) and malignancies is rapidly increasing worldwide and has become a major disease that threatens human life. In this study, we quantitatively analyzed the proteome of tumor tissues and adjacent normal tissues from six patients withT2DM combined with colorectal cancer (CRC) and eight non-diabetic CRC, focusing on the effect of T2DM on tumor tissues. We analyzed the functional enrichment of differentially expressed proteins (DEPs) using clusterProfiler in R and the expression level of protein tyrosine phosphatase non-receptor type 11 (PTPN11) and other key proteins in the TIMER and GEPIA2 databases. The HPA database was used to validate PTPN11 protein expression. The correlation between PTPN11 expression and clinicopathological features was analyzed by UALCAN database. The impact of PTPN11 on clinical prognosis was evaluated utilizing Kaplan-Meier Plotter. The correlation between PTPN11 expression and tumor-infiltrated immune cells was investigated via TIMER and TISIDB databases. Gene set enrichment analysis (GSEA) was performed to examined the pathway of PTPN11 enrichment in CRC using data from The Cancer Genome Atlas (TCGA) database. Furthermore, small interfering (si) RNA was used to knock down PTPN11 in CRC cell line SW480. Western blot analysis was used to detect PTPN11 expression in tissue samples or cells and the effect of PTPN11 knockdown on key proteins related to PI3K/AKT and cell cycle pathway in SW480 cells. Cell proliferation and wound healing assays were used to detect the effects of cell proliferation and migration after knockdown of PTPN11 or treatment with high glucose. We found that metabolic pathways such as oxidative phosphorylation, glycolysis/gluconeogenesis, and insulin secretion were significantly enriched in tumor tissues from diabetic patients compared to non-diabetic patients. In addition, PTPN11, a marker gene associated with T2DM and CRC, were mined in diabetic tumor tissues. PTPN11 showed high expression in diabetic tumor tissues compared to normal tissues. High PTPN11 expression predicted poor prognosis in CRC. PTPN11 expression was strongly associated with immune infiltrating cells in CRC. GSEA analysis revealed that PTPN11 was enriched in cancer-related pathways. Western blotting analysis indicated that PTPN11 knockdown reduced the protein levels of p-PI3K, p-AKT, CDK1 and CYCLIN D, without altering PI3K and AKT protein levels. Cell proliferation and wound healing data showed that PTPN11 and high glucose could increase the proliferation and migration ability. These findings showed that PTPN11 may be a potential key biomarker for CRC in patients with diabetes, which will provide new potential targets for future intervention of T2DM complicated with CRC.

Integrating machine learning and bioinformatics approaches for identifying novel diagnostic gene biomarkers in colorectal cancer.

This study aimed to identify diagnostic gene biomarkers for colorectal cancer (CRC) by analyzing differentially expressed genes (DEGs) in tumor and adjacent normal samples across five colon cancer gene-expression profiles (GSE10950, GSE25070, GSE41328, GSE74602, GSE142279) from the Gene Expression Omnibus (GEO) database. Intersecting identified DEGs with the module with the highest correlation to gene expression patterns of tumor samples in the gene co-expression network analysis revealed 283 overlapped genes. Centrality measures were calculated for these genes in the reconstructed STRING protein-protein interaction network. Applying LASSO logistic regression, eleven genes were ultimately recognized as candidate diagnostic genes. Among these genes, the area under the receiver operating characteristic curve (AUROC) values for nine genes (CDC25B, CDK4, IQGAP3, MMP1, MMP7, SLC7A5, TEAD4, TRIB3, and UHRF1) surpassed the threshold of 0.92 in both the training and validation sets. We evaluated the diagnostic performance of these genes with four machine learning algorithms: random forest (RF), support vector machines (SVM), artificial neural network (ANN), and gradient boosting machine (GBM). In the testing dataset (GSE21815 and GSE106582), the AUROC scores were greater than 0.95 for all of the machine learning algorithms, indicating the high diagnostic performance of the nine genes. Besides, these nine genes are also significantly correlated to twelve immune cells, namely Mast cells activated, Macrophages M0, M1, and M2, Neutrophils, T cells CD4 memory activated, T cells follicular helper, T cells CD8, T cells CD4 memory resting, B cells memory, Plasma cells, and Mast cells resting (P < 0.05). These results strongly suggest that all of the nine genes have the potential to serve as reliable diagnostic biomarkers for CRC.

COL25A1 and METAP1D DNA methylation are promising liquid biopsy epigenetic biomarkers of colorectal cancer using digital PCR

BackgroundColorectal cancer is a public health issue and was the third leading cause of cancer-related death worldwide in 2022. Early diagnosis can improve prognosis, making screening a central part of colorectal cancer management. Blood-based screening, diagnosis and follow-up of colorectal cancer patients are possible with the study of cell-free circulating tumor DNA. This study aimed to identify novel DNA methylation biomarkers of colorectal cancer that can be used for the follow-up of patients with colorectal cancer.MethodsA DNA methylation profile was established in the Gene Expression Omnibus (GEO) database (n = 507) using bioinformatics analysis and subsequently confirmed using The Cancer Genome Atlas (TCGA) database (n = 348). The in silico profile was then validated on local tissue and cell-free DNA samples using methylation-specific digital PCR in colorectal cancer patients (n = 35) and healthy donors (n = 35).ResultsThe DNA methylation of COL25A1 and METAP1D was predicted to be a colorectal cancer biomarker by bioinformatics analysis (ROC AUC = 1, 95% CI [0.999–1]). The two biomarkers were confirmed with tissue samples, and the combination of COL25A1 and METAP1D yielded 49% sensitivity and 100% specificity for cell-free DNA.ConclusionBioinformatics analysis of public databases revealed COL25A1 and METAP1D DNA methylation as clinically applicable liquid biopsies DNA methylation biomarkers. The specificity implies an excellent positive predictive value for follow-up, and the high sensitivity and relative noninvasiveness of a blood-based test make these biomarkers compatible with colorectal cancer screening. However, the clinical impact of these biomarkers in colorectal cancer screening and follow-up needs to be established in further prospective studies.

Guanine nucleotide exchange factors and colon neoplasia

Despite many diagnostic and therapeutic advances, colorectal cancer (CRC) remains the second leading cause of cancer death for men and women in the United States. Alarmingly, for reasons currently unknown, the demographics of this disease have shifted towards a younger population. Hence, understanding the molecular mechanisms underlying CRC initiation and progression and leveraging these findings for therapeutic purposes remains a priority. Here, we review critically the evidence that canonical and noncanonical actions of guanine nucleotide exchange factors (GEFs) play important roles in CRC evolution. Rho GEF GTPases, which switch between inactive GDP-bound and active GTP-bound states, are commonly overexpressed and activated in a variety of cancers, including CRC, and may be tractable therapeutic targets. In addition to comprehensively reviewing this field, we focus on Rho/Rac GEFs that are involved in regulating key functions of normal and neoplastic cells like cell polarity, vesicle trafficking, cell cycle regulation, and transcriptional dynamics. Prime examples of such Rho/Rac GEFs include βPak-interacting exchange factor (βPix), a Rho family GEF for Cdc42/Rac1, Tiam1, GEF-H1, RGNEF, and other GEFs implicated in CRC development and progression. Throughout this analysis, we explore how these findings fill key gaps in knowledge regarding the molecular basis of colon carcinogenesis and how they may be leveraged to treat advanced CRC. Lastly, we address potential future directions for research into the role of GEFs as CRC biomarkers and therapeutic targets. In this regard, leveraging the noncanonical actions of GEFs appears to provide a relatively unexplored opportunity requiring further investigation.

CircLPHN3 correlates with prognosis in colorectal cancer and regulates cellular processes by targeting miR-142-5p.

Colorectal cancer (CRC) is often diagnosed late and has a poor prognosis. Circular RNAs (circRNAs) have been identified as prognostic biomarkers in various cancers, including CRC. The objective was to elucidate the role of circLPHN3 (hsa_circ_0069865) in CRC progression and to provide a promising prognostic marker for CRC. CircLPHN3 was identified through bioinformatics analysis of the GSE121842 dataset. The levels of circLPHN3 in CRC samples were analyzed by real time-quantitative polymerase chain reaction. Its clinical significance was assessed using the Kaplan-Meier curve and multivariate Cox regression. Downstream microRNAs of circLPHN3 were predicted with the RNAhybrid, Circular RNA Interactome, and starBase online databases. The target of miR-142-5p was predicted using miRDB, TargetScanHuman, starBase, and miRWalk databases. The relationship between circLPHN3, miR-142-5p, and LDB2 was verified by dual luciferase reporter assay. The function of circLPHN3 on CRC cell growth and metastasis was measured using Transwell and the cell counting kit-8 assay. Significant downregulation of circLPHN3 was found in CRC. CircLPHN3 was closely related to higher tumor node metastasis stage, lymph node metastasis, and predicted unfavorable prognosis. miR-142-5p was highly expressed in CRC and its expression was negatively regulated by circLPHN3. Overexpression of circLPHN3 curbed CRC cell growth, migration, and invasion, mediated by miR-142-5p. Moreover, LDB2 was identified as a target of miR-142-5p. CircLPHN3 acted as a prognostic biomarker and tumor suppressor for CRC via modulating miR-142-5p.

Some Glycoproteins Expressed on the Surface of Immune Cells and Cytokine Plasma Levels Can Be Used as Potential Biomarkers in Patients with Colorectal Cancer

Colorectal cancer (CRC) is a leading cause of mortality worldwide. Its incidence holds a major position among the most common life-threatening diseases. Hence, the early identification and precise characterization of disease activity based on proper biomarkers are of utmost importance for therapeutic strategy and patient survival. The identification of new biomarkers for colorectal cancer or disease-specific levels/combinations of biomarkers will significantly contribute to precise diagnosis and improved personalized treatment of patients. Therefore, the present study aims to identify colorectal cancer-specific immunological biomarkers. The plasma levels of several cytokines (interleukin-1β /IL-1β/, IL-2, IL-4, IL-10, IL-12, IL-15, TGFβ and IFNγ) of 20 patients with colorectal cancer and 21 healthy individuals were determined by ELISA. The expression of several types of glycoproteins on the surface of peripheral blood leukocytes isolated from CRC patients and healthy volunteers was evaluated by flow cytometry. Correlations between cytokine levels and cell surface glycoprotein expression were analyzed. The obtained results demonstrated significantly elevated levels of CD80, CD86, CD279 and CD274 expressing leukocyte populations in the cancer patient group, while the numbers of NK cells and CD8- and CD25-positive cells were decreased. Based on these data and the correlations with cytokine levels, it can be concluded that CD25, CD80, CD86, CD274 and CD279 glycoproteins combined with specific plasma levels of IL-1β, IL-2, IL-15 and TGFβ could represent potential biomarkers for colorectal cancer.

The prognostic role of circulating tumour DNA detected prior to clinical diagnosis of colorectal cancer in the HUNT study

BackgroundToday, the prognostic tools available at the time of diagnosis in colorectal cancer (CRC) are limited. Better prognostic tools are a prerequisite for personalised treatment.This study aimed to investigate whether circulating tumour DNA (ctDNA) markers found in plasma before clinical diagnosis of CRC could contribute to the prediction of poor prognosis.MethodsThis observational cohort study included patients diagnosed with CRC stage I-III within 24 months following participation in the Trøndelag Health Study (n = 85). Known methylated ctDNA biomarkers of CRC were analysed by PCR in plasma. Outcomes were overall survival (OS), recurrence-free survival (RFS) and poor prognosis (PP). Candidate clinical and methylated ctDNA predictors of the outcomes were identified by Cox regression analyses.ResultsMethylated GRIA4 (HR 1.96 (1.06–3.63)), RARB (HR 9.48 (3.00–30.00)), SLC8A1 (HR 1.97 (1.03–3.77)), VIM (HR 2.95 (1.22–7.14)) and WNT5A (HR 5.83 (2.33–14.56)) were independent predictors of OS, methylated RARB (HR 9.67 (2.54–36.81)), SDC2 (HR 3.38 (1.07–10.66)), SLC8A1 (HR 2.93 (1.01–8.51)) and WNT5A (HR 6.95 (1.81–26.68)) were independent predictors of RFS and methylated RARB (HR 6.11 (1.69–22.18)), SDC2 (HR 2.79 (1.20–6.49)) and WNT5A (HR 5.57 (3.04–15.26)) were independent predictors of PP (p < 0.05).ConclusionsPrediagnostic ctDNA markers are promising contributors to predicting poor prognosis in CRC, potentially becoming one of the tools guiding more personalised treatment.

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.

P50-4 PiRNA-823 as a prognostic, non-invasive biomarker in colorectal cancer: a systematic review

P50-4 PiRNA-823 as a prognostic, non-invasive biomarker in colorectal cancer: a systematic review

Potential of Pre-Operative Serum Interleukin-6 as a Biomarker for Colorectal Cancers

The diagnosis of colorectal cancers (CRC) at its early stage is challenging due to lack of early markers. Current diagnostic tests are either invasive or show low sensitivity. Interleukins are known to elevate and play important roles in the development and progression of the CRC. The studies on interleukin profiles of CRC patients are mainly confined to Caucasian populations while South Asian data are sparse. Therefore, the aim of this study was to investigate the serum IL-6 and IL-10 levels in a cohort of Sri Lankan CRC patients and explore their potential to be used as markers for early diagnosis/prognosis of CRC. Blood samples from 35 CRC patients and 35 healthy volunteers were obtained after informed consent. Their clinical findings and carcinoembryonic antigen (CEA) levels were recorded. Concentrations of IL-6 and IL-10 were measured using ELISA according to manufacturer’s protocols. Mean serum [IL-6] was found to be significantly higher in CRC patients than controls (p&lt;0.05). The mean [IL-10]showed no difference to that of controls. (p&gt;0.05). Interestingly, the [IL-6] in CRC patients were correlated with the disease stage (Stage I-0.16pg/ml; stage II-7.01pg/ml; stage III-15.8pg/ml and stage IV-35.48pg/ml). CEA levels were not correlated with the disease stage or withIL-6 levels. This study provided preliminary evidence to use IL-6 as a potential biochemical marker for the diagnosis of CRC inaddition to CEA. Furthermore, IL-6 could be a marker for prognosis of CRC. Further studies with higher patient samples are needed to validate the results of this study.

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.

Identifying Colibactin-Producing Escherichia coli as Candidate Biomarker for Aggressive Colorectal Cancer

Identifying Colibactin-Producing Escherichia coli as Candidate Biomarker for Aggressive Colorectal Cancer

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.

Long non‑coding RNAs as diagnostic and prognostic biomarkers for colorectal cancer (Review).

Colorectal cancer (CRC) ranks as the 3rd most common cancer globally and is the 2nd leading cause of cancer-related death. Owing to the lack of specific early symptoms and the limitations of existing early diagnostic methods, most patients with CRC are diagnosed at advanced stages. To overcome these challenges, researchers have increasingly focused on molecular biomarkers, with particular interest in long non-coding RNAs (lncRNAs). These non-protein-coding RNAs, which exceed 200 nucleotides in length, play critical roles in the development and progression of CRC. The stability and detectability of lncRNAs in the circulatory system make them promising candidate biomarkers. The analysis of circulating lncRNAs in peripheral blood represents a potential option for minimally invasive diagnostic tests based on liquid biopsy samples. The present review aimed to evaluate the efficacy of lncRNAs with altered expression levels in peripheral blood as diagnostic markers for CRC. Additionally, the clinical significance of lncRNAs as prognostic markers for this disease were summarized.

Enhanced SERS detection of the colorectal cancer biomarker utilizing a two-dimensional silver substrate

To improve the sensitivity, accuracy and specificity of the assay, a two-dimensional silver substrate with EF=5.85×108 was first synthesized as a SERS substrate, on the surface of which DSP molecules were modified to form a DSP-antibody coupling through the activation of two N-hydroxy succinimide (NHS) esters to capture TNF-α. Subsequently, aptamerized silver-coated gold nanospheres (Au@TFMBA@Ag) were synthesized as Surface-Enhanced Raman Scattering (SERS) recognition probes. These probes were employed to create a sandwich structure for the quantitative detection of Tumor Necrosis Factor-alpha (TNF-α), utilizing the SERS signal intensity at 1374 cm−1. Quantitative detection of TNF-α was successfully accomplished within the concentration range of 10−4 to 10−10 mg·mL−1. Clinical serum samples were collected and subjected to testing. Significance analysis, conducted through the T-test (p < 0.0001), unequivocally showed the method's ability to differentiate between sera from normal individuals and those diagnosed with colon cancer.

Predictive nomogram of the clinical outcomes of colorectal cancer based on methylated SEPT9 and intratumoral IL-10+ Tregs infiltration

BackgroundGene methylation and the immune-related tumor microenvironment (TME) are highly correlated in tumor progression and therapeutic efficacy. Although both of them can be used to predict the clinical outcomes of colorectal cancer (CRC) patients, their predictive value is still unsatisfactory. Whether a combination risk model comprising these two prediction parameters performs better predictive effectiveness than independent factor is still unclear. Methylated Septin9 (mSEPT9) is an early diagnosis biomarker of CRC, in this study, we aimed to investigate mSEPT9-related biomarkers of immunosuppressive TME and identify the value of the combination risk model in predicting the clinical outcomes of CRC.MethodsImmunofluorescence staining was performed to clarify the correlation between intratumoral IL-10+ Treg infiltration and mSEPT9 in peripheral blood. Survival time, response to 5-fluorouracil (5-FU)-based chemotherapy and PD-1 blockade, and the probability of recurrence or metastasis were analyzed in study (197 CRC samples) and validation (195 CRC samples) sets to evaluate the efficacy of combination risk model. Potential mechanisms were explored by mRNA sequencing.ResultsHypermethylated SEPT9 in the peripheral blood of patients with CRC (stage I-III, and stage IV with resectable M1) before radical resection was positively correlated with high intratumoral IL-10+ Treg infiltration. The high-risk model revealed poor overall survival and progression-free survival, inferior therapeutic response to 5-FU-based chemotherapy and PD-1 blockade, and high probability of recurrence or metastasis. The underlying mechanisms may be associated with the increase in mSEPT9 and mediation of IL-10 via methionine metabolic reprogramming-induced infiltration of IL-10+ Tregs in the TME, which promotes tumor progression and resistance to 5-FU-based chemotherapy and PD-1 blockade.ConclusionsThe combination risk model of peripheral mSETP9 and intratumoral IL-10+ Treg infiltration in CRC can effectively predict prognosis, responsiveness to 5-FU-based chemotherapy and PD-1 blockade, and the probability of recurrence or metastasis. Therefore, this model can be used for precision treatment of CRC.