A Novel Blood-Based Colorectal Cancer Diagnostic Technology Using Electrical Detection of Colon Cancer Secreted Protein-2

Blood-based biomarkers and novel technologies for the diagnosis of colorectal cancer and adenomas: a narrative review

Aim: Blood-based biomarkers have shown promise for diagnosing colorectal cancer (CRC) and adenomas (CRA). This review summarizes recent studies in this area. Methods: A literature search was undertaken for 01/01/2017-01/03/2023. Criteria included CRC, CRA, liquid-biopsy, blood-based tests and diagnosis. Results: 12,378 studies were reduced to 178 for data extraction. Sixty focused on proteomics, 53 on RNA species, 30 on cfDNA methylation, seven on antigens and autoantibodies and 28 on novel techniques. 169 case control and nine cohort studies. Number of participants ranged 100-54,297, mean age 58.26. CRC sensitivity and specificity ranged 9.10-100% and 20.40-100%, respectively. CRA sensitivity and specificity ranged 8.00-95.70% and 4.00-97.00%, respectively. Conclusion: Sensitive and specific blood-based tests exist for CRC and CRA. However, studies demonstrate heterogenous techniques and reporting quality. Further work should concentrate on validation and meta-analyzes.

Mobile Point-of-Care Device Using Molecularly Imprinted Polymer-Based Chemosensors Targeting Interleukin-1β Biomarker

Molecularly imprinted polymers (MIPs) have garnered significant attention as a promising material for engineering specific biological receptors with superior chemical complementarity to target molecules. In this study, we present an electrochemical biosensing platform incorporating MIP films for the selective detection of the interleukin-1β (IL-1β) biomarker, particularly suitable for mobile point-of-care testing (POCT) applications. The IL-1β-imprinted biosensors were composed of poly(eriochrome black T (EBT)), including an interlayer of poly(3,4-ethylene dioxythiophene) and a 4-aminothiophenol monolayer, which were electrochemically polymerized simultaneously with template proteins (i.e., IL-1β) on custom flexible screen-printed carbon electrodes (SPCEs). The architecture of the MIP films was designed to enhance the sensor sensitivity and signal stability. This approach involved a straightforward sequential-electropolymerization process and extraction for leaving behind cavities (i.e., rebinding sites), resulting in the efficient production of MIP-based biosensors capable of molecular recognition for selective IL-1β detection. The electrochemical behaviors were comprehensively investigated using cyclic voltammograms and electrochemical impedance spectroscopy responses to assess the imprinting effect on the MIP films formed on the SPCEs. In line with the current trend in in vitro diagnostic medical devices, our simple and effective MIP-based analytical system integrated with mobile POCT devices offers a promising route to the rapid detection of biomarkers, with particular potential for periodontitis screening.

Interface-Engineered Field-Effect Transistor Electronic Devices for Biosensing

Promising advances in molecular medicine have promoted the urgent requirement for reliable and sensitive diagnostic tools. Electronic biosensing devices based on field-effect transistors (FETs) exhibit a wide range of benefits, including rapid and label-free detection, high sensitivity, easy operation, and capability of integration, possessing significant potential for application in disease screening and health monitoring. In this perspective, the tremendous efforts and achievements in the development of high-performance FET biosensors in the past decade are summarized, with emphasis on the interface engineering of FET-based electrical platforms for biomolecule identification. First, an overview of engineering strategies for interface modulation and recognition element design is discussed in detail. For a further step, the applications of FET-based electrical devices for in vitro detection and real-time monitoring in biological systems are comprehensively reviewed. Finally, the key opportunities and challenges of FET-based electronic devices in biosensing are discussed. It is anticipated that a comprehensive understanding of interface engineering strategies in FET biosensors will inspire additional techniques for developing highly sensitive, specific, and stable FET biosensors as well as emerging designs for next-generation biosensing electronics.

Progress in the field of noninvasive diagnostics for colorectal cancer: a systematic review for the accuracy of blood-based biomarkers for detection of advanced pre-cancerous lesions

BACKGROUND

Early detection of pre-cancerous adenomas through screening can reduce colorectal cancer (CRC) incidence. Fecal immunochemical tests are commonly used, but have limited sensitivity for pre-cancerous lesions. Blood-based screening may improve test sensitivity. This systematic review and meta-analysis was conducted to evaluate the accuracy of blood-based biomarkers for detection of advanced pre-cancerous lesions.

RESEARCH DESIGN AND METHODS

We present the accuracy of blood-based biomarkers for the detection of advanced pre-cancerous lesions. EMBASE, Web of Science and PubMed databases were searched, with study populations limited to adults diagnosed with advanced pre-cancerous lesions at colonoscopy, who had a blood-based biomarker test analyzed with reports of sensitivity and specificity.

RESULTS

69 studies were identified, which assessed 133 unique biomarkers sets. The best performing test was a panel of 6 miRNAs, with a sensitivity of 95% and specificity of 90% for advanced pre-cancerous lesions. Only 6 biomarkers demonstrated sensitivity ≥ 50% and specificity ≥ 90% for the detection of advanced pre-cancerous lesions.

CONCLUSION

Many different blood-based biomarkers have been assessed for detection of advanced pre-cancerous lesions, but few have progressed beyond the discovery stage. While some biomarkers have reported high sensitivity and specificity, larger prospective studies in unbiased intended-use screening populations are required for validation.

Construction and Validation of a Protein-associated Prognostic Model for Gastrointestinal Cancer

Background Gastrointestinal cancer (GIC) is a prevalent and lethal malignant tumor. It is obligatory to investigate innovative biomarkers for the diagnosis and prognosis. Proteins play a crucial role in regulating the occurrence and progression of GIC. However, the prognostic value of proteins is unclear in GIC.

OBJECTIVE

This paper aims to identify the hub prognosis-related proteins (PAPs) and construct a prognosis model for GIC patients for clinical application.

METHODS

Protein expression data of GIC was obtained from The Cancer Proteome Atlas (TCPA) and downloaded the clinicopathological data from The Cancer Genome Atlas database (TCGA). Besides, hub proteins were filtrated via univariate and multivariate Cox regression analysis. Moreover, survival analysis and nomogram were used to predict overall survival (OS). We used the calibration curves to assess the consistency of predictive and actual survival rates. The consistency index (C-index) was used to evaluate the prognostic ability of the predictive model. Furthermore, functional enrichment analysis and protein co-expression of PAPs were used to explore their roles in GIC.

RESULTS

Finally, a prognosis model was conducted based on ten PAPs (CYCLIND1, DVL3, NCADHERIN, SYK, ANNEXIN VII, CD20, CMET, RB, TFRC, and PREX1). The risk score calculated by the model was an independent prognostic predictor. Compared with the high-risk subgroup, the low-risk subgroup had better OS. In the TCGA cohort, the area under the curve value of the receiver operating characteristic curve of the prognostic model was 0.692. The expression of proteins and risk score had a significant association with the clinicopathological characteristics of GIC. Besides, a nomogram based on GIC clinicopathological features and risk scores could properly predict the OS of individual GIC patients. The C-index is 0.71 in the TCGA cohort and 0.73 in the GEO cohort.

CONCLUSION

The results indicate that the risk score is an independent prognostic biomarker and is related to the malignant clinical features of GIC patients. Besides, several PAPs associated with the survival and clinicopathological characteristics of GIC might be potential biomarkers for GIC diagnosis and treatment.

The "Bloodless" Blood Test: Intradermal Prick Nanoelectronics for the Blood Extraction-Free Multiplex Detection of Protein Biomarkers

Protein biomarkers' detection is of utmost importance for preventive medicine and early detection of illnesses. Today, their detection relies entirely on clinical tests consisting of painful, invasive extraction of large volumes of venous blood; time-consuming postextraction sample manipulation procedures; and mostly label-based complex detection approaches. Here, we report on a point-of-care (POC) diagnosis paradigm based on the application of intradermal finger prick-based electronic nanosensors arrays for protein biomarkers' direct detection and quantification down to the sub-pM range, without the need for blood extraction and sample manipulation steps. The nanobioelectronic array performs biomarker sensing by a rapid intradermal prick-based sampling of proteins biomarkers directly from the capillary blood pool accumulating at the site of the microneedle puncture, requiring only 2 min and less than one microliter of a blood sample for a complete analysis. A 1 mm long microneedle element was optimal in allowing for pain-free dermal sampling with a 100% success rate of reaching and rupturing dermis capillaries. Current common micromachining processes and top-down fabrication techniques allow the nanobioelectronic sensor arrays to provide accurate and reliable clinical diagnostic results using multiple sensing elements in each microneedle and all-in-one direct and label-free multiplex biomarkers detection. Preliminary successful clinical studies performed on human volunteers demonstrated the ability of our intradermal, in-skin, blood extraction-free detection platform to accurately detect protein biomarkers as a plausible POC detection for future replacement of today's invasive clinical blood tests. This approach can be readily extended in the future to detect other clinically relevant circulating biomarkers, such as miRNAs, free-DNAs, exosomes, and small metabolites.

Advances in High Throughput Proteomics Profiling in Establishing Potential Biomarkers for Gastrointestinal Cancer

Gastrointestinal cancers (GICs) remain the most diagnosed cancers and accounted for the highest cancer-related death globally. The prognosis and treatment outcomes of many GICs are poor because most of the cases are diagnosed in advanced metastatic stages. This is primarily attributed to the deficiency of effective and reliable early diagnostic biomarkers. The existing biomarkers for GICs diagnosis exhibited inadequate specificity and sensitivity. To improve the early diagnosis of GICs, biomarkers with higher specificity and sensitivity are warranted. Proteomics study and its functional analysis focus on elucidating physiological and biological functions of unknown or annotated proteins and deciphering cellular mechanisms at molecular levels. In addition, quantitative analysis of translational proteomics is a promising approach in enhancing the early identification and proper management of GICs. In this review, we focus on the advances in mass spectrometry along with the quantitative and functional analysis of proteomics data that contributes to the establishment of biomarkers for GICs including, colorectal, gastric, hepatocellular, pancreatic, and esophageal cancer. We also discuss the future challenges in the validation of proteomics-based biomarkers for their translation into clinics.

SARS-CoV-2 Variant Screening Using a Virus-Receptor-Based Electrical Biosensor

SARS-CoV-2 variants are of particular interest because they can potentially increase the transmissibility and virulence of COVID-19 or reduce the effectiveness of available vaccines. However, screening SARS-CoV-2 variants is a challenge because biosensors target viral components that can mutate. One promising strategy is to screen variants via angiotensin-converting enzyme 2 (ACE2), a virus receptor shared by all known SARS-CoV-2 variants. Here we designed a highly sensitive and portable COVID-19 screening biosensor based on the virus receptor. We chose a dual-gate field-effect transistor to overcome the low sensitivity of virus-receptor-based biosensors. To optimize the biosensor, we introduced a synthetic virus that mimics the important features of SARS-CoV-2 (size, bilayer structure, and composition). The developed biosensor successfully detected SARS-CoV-2 in 20 min and showed sensitivity comparable to that of molecular diagnostic tests (∼165 copies/mL). Our results indicate that a virus-receptor-based biosensor can be an effective strategy for screening infectious diseases to prevent pandemics.

Evolution and diversification of the nuclear envelope

Eukaryotic cells arose ~1.5 billion years ago, with the endomembrane system a central feature, facilitating evolution of intracellular compartments. Endomembranes include the nuclear envelope (NE) dividing the cytoplasm and nucleoplasm. The NE possesses universal features: a double lipid bilayer membrane, nuclear pore complexes (NPCs), and continuity with the endoplasmic reticulum, indicating common evolutionary origin. However, levels of specialization between lineages remains unclear, despite distinct mechanisms underpinning various nuclear activities. Several distinct modes of molecular evolution facilitate organellar diversification and  to understand which apply to the NE, we exploited proteomic datasets of purified nuclear envelopes from model systems for comparative analysis. We find enrichment of core nuclear functions amongst the widely conserved proteins to be less numerous than lineage-specific cohorts, but enriched in core nuclear functions. This, together with consideration of additional evidence, suggests that, despite a common origin, the NE has evolved as a highly diverse organelle with significant lineage-specific functionality.

Updates on Clinical Use of Liquid Biopsy in Colorectal Cancer Screening, Diagnosis, Follow-Up, and Treatment Guidance

Colorectal cancer (CRC) is one of the most common cancers worldwide, being the third most diagnosed in the world and the second deadliest. Solid biopsy provides an essential guide for the clinical management of patients with colorectal cancer; however, this method presents several limitations, in particular invasiveness, and cannot be used repeatedly. Recently, clinical research directed toward the use of liquid biopsy, as an alternative tool to solid biopsy, showed significant promise in several CRC clinical applications, as (1) detect CRC patients at early stage, (2) make treatment decision, (3) monitor treatment response, (4) predict relapses and metastases, (5) unravel tumor heterogeneity, and (6) detect minimal residual disease. The purpose of this short review is to describe the concept, the characteristics, the genetic components, and the technologies used in liquid biopsy in the context of the management of colorectal cancer, and finally we reviewed gene alterations, recently described in the literature, as promising potential biomarkers that may be specifically used in liquid biopsy tests.

Sensitive detection of colorectal cancer in peripheral blood by a novel methylation assay

BACKGROUND

Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide. Early detection of CRC can significantly reduce its mortality rate. Current method of CRC diagnosis relies on the invasive endoscopy. Non-invasive assays including fecal occult blood testing (FOBT) and fecal immunological test (FIT) are compromised by low sensitivity and specificity, especially at early stages. Thus, a non-invasive and accurate approach for CRC screening would be highly desirable.

RESULTS

A new qPCR-based assay combining the simultaneous detection of the DNA methylation status of ten candidate genes was used to examine plasma samples from 56 normal controls, 6 hyperplastic polys, 9 non-advanced adenomas (NAAs), 22 advanced adenomas (AAs) and 175 CRC patients, using 10 ng of cfDNA. We further built a logistic regression model for CRC diagnosis. We tested ten candidate methylation markers including twist1, vav3-as1, fbn1, c9orf50, sfmbt2, kcnq5, fam72c, itga4, kcnj12 and znf132. All markers showed moderate diagnostic performance with AUCs ranging from 0.726 to 0.815. Moreover, a 4-marker model, comprised of two previously reported markers (c9orf50 and twist1) and two novel ones (kcnj12 and znf132), demonstrated high performance for detecting colorectal cancer in an independent validation set (N = 69) with an overall AUC of 0.911 [95% confidence interval (CI) 0.834-0.988], sensitivity of 0.800 [95% CI 0.667-0.933] and specificity of 0.971 [95% CI 0.914-1.000]. The stage-stratified sensitivity of the model was 0.455 [95% CI 0.227-0.682], 0.667 [95% CI 0.289-1.000], 0.800 [95% CI 0.449-1.000], 0.800 [95% CI 0.449-1.000] and 0.842 [95% CI 0.678-1.000] for advanced adenoma and CRC stage I-IV, respectively.

CONCLUSION

kcnj12 and znf132 are two novel methylation biomarkers for CRC diagnosis. The 4-marker methylation model provides a new non-invasive choice for CRC screening and interception.

Construction and validation of an RNA-binding protein-associated prognostic model for colorectal cancer

Colorectal cancer (CRC) is one of the most prevalent and fatal malignancies, and novel biomarkers for the diagnosis and prognosis of CRC must be identified. RNA-binding proteins (RBPs) are essential modulators of transcription and translation. They are frequently dysregulated in various cancers and are related to tumorigenesis and development. The mechanisms by which RBPs regulate CRC progression are poorly understood and no clinical prognostic model using RBPs has been reported in CRC. We sought to identify the hub prognosis-related RBPs and to construct a prognostic model for clinical use. mRNA sequencing and clinical data for CRC were obtained from The Cancer Genome Atlas database (TCGA). Gene expression profiles were analyzed to identify differentially expressed RBPs using R and Perl software. Hub RBPs were filtered out using univariate Cox and multivariate Cox regression analysis. We used functional enrichment analysis, including Gene Ontology and Gene Set Enrichment Analysis, to perform the function and mechanisms of the identified RBPs. The nomogram predicted overall survival (OS). Calibration curves were used to evaluate the consistency between the predicted and actual survival rate, the consistency index (c-index) was calculated, and the prognostic effect of the model was evaluated. Finally, we identified 178 differently expressed RBPs, including 121 up-regulated and 57 down-regulated proteins. Our prognostic model was based on nine RBPs (PNLDC1, RRS1, HEXIM1, PPARGC1A, PPARGC1B, BRCA1, CELF4, AEN and NOVA1). Survival analysis showed that patients in the high-risk subgroup had a worse OS than those in the low-risk subgroup. The area under the curve value of the receiver operating characteristic curve of the prognostic model is 0.712 in the TCGA cohort and 0.638 in the GEO cohort. These results show that the model has a moderate diagnostic ability. The c-index of the nomogram is 0.77 in the TCGA cohort and 0.73 in the GEO cohort. We showed that the risk score is an independent prognostic biomarker and that some RBPs may be potential biomarkers for the diagnosis and prognosis of CRC.

Electrical Cartridge Sensor Enables Reliable and Direct Identification of MicroRNAs in Urine of Patients

Urinary miRNAs are biomarkers that demonstrate considerable promise for the noninvasive diagnosis and prognosis of diseases. However, because of background noise resulting from complex physiological features of urine, instability of miRNAs, and their low concentration, accurate monitoring of miRNAs in urine is challenging. To address these limitations, we developed a urine-based disposable and switchable electrical sensor that enables reliable and direct identification of miRNAs in patient urine. The proposed sensing platform combining disposable sensor chips composed of a reduced graphene oxide nanosheet and peptide nucleic acid facilitates the label-free detection of urinary miRNAs with high specificity and sensitivity. Using real-time detection of miRNAs in patient urine without pretreatment or signal amplification, this sensor allows rapid, direct detection of target miRNAs in a broad dynamic range with a detection limit down to 10 fM in human urine specimens within 20 min and enables simultaneous quantification of multiple miRNAs. As confirmed using a blind comparison with the results of pathological examination of patients with prostate cancer, the sensor offers the potential to improve the accuracy of early diagnosis before a biopsy is taken. This study holds the usefulness of the practical sensor for the clinical diagnosis of urological diseases.

Noninvasive Precision Screening of Prostate Cancer by Urinary Multimarker Sensor and Artificial Intelligence Analysis

Screening for prostate cancer relies on the serum prostate-specific antigen test, which provides a high rate of false positives (80%). This results in a large number of unnecessary biopsies and subsequent overtreatment. Considering the frequency of the test, there is a critical unmet need of precision screening for prostate cancer. Here, we introduced a urinary multimarker biosensor with a capacity to learn to achieve this goal. The correlation of clinical state with the sensing signals from urinary multimarkers was analyzed by two common machine learning algorithms. As the number of biomarkers was increased, both algorithms provided a monotonic increase in screening performance. Under the best combination of biomarkers, the machine learning algorithms screened prostate cancer patients with more than 99% accuracy using 76 urine specimens. Urinary multimarker biosensor leveraged by machine learning analysis can be an important strategy of precision screening for cancers using a drop of bodily fluid.

Research advances of secretory proteins in malignant tumors

Secretory proteins in tumor tissues are important components of the tumor microenvironment. Secretory proteins act on tumor cells or stromal cells or mediate interactions between tumor cells and stromal cells, thereby affecting tumor progression and clinical treatment efficacy. In this paper, recent research advances in secretory proteins in malignant tumors are reviewed.

Identification prognosis-associated immune genes in colon adenocarcinoma

Colon adenocarcinoma (COAD) is one of the most prevalent malignant tumors worldwide. Immune genes (IGs) have a considerable correlation with tumor initiation and prognosis. The present paper aims to identify the prognosis value of IGs in COAD and conduct a prognosis model for clinical utility. Gene expression data of COAD were downloaded from The Cancer Genome Atlas (TCGA), screening and analyzing differentially expressed IGs by bioinformatics. Core genes were screened by univariate and multivariate Cox regression analyses. Survival analysis was appraised by the Kaplan-Meier method and the log-rank test. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis (GSEA) were used to identify IGs' relevant signal pathways. We predicted the overall survival (OS) by nomogram. Finally, a prognosis model was conducted based on 12 IGs (SLC10A2, CXCL3, NOX4, FABP4, ADIPOQ, IGKV1-33, IGLV6-57, INHBA, UCN, VIP, NGFR, and TRDC). The risk score was an independent prognostic factor, and a nomogram could accurately predict the OS of individual COAD patients. These results were validated in GSE39582, GSE12945, and GSE103479 cohorts. Functional enrichment analysis demonstrated that these IGs are mainly enriched in hormone secretion, hormone transport, lipid transport, cytokine-cytokine receptor interaction, and peroxisome proliferators-activated receptor signaling pathway. In summary, the risk score is an independent prognostic biomarker. We also excavated several IGs related to COAD's survival and maybe potential biomarkers for COAD diagnosis and treatment.

Identification of prognosis-associated immune genes and exploration of immune cell infiltration in colorectal cancer

Aim: To identify prognosis-related immune genes (PRIGs) and construct a prognosis model of colorectal cancer (CRC) patients for clinical use. Materials & methods: Expression profiles were obtained from The Cancer Genome Atlas database and identified differentially expressed PRIGs of CRC. Results: A prognostic model was conducted based on nine PRIGs. The risk score, based on prognosis model, was an independent prognostic predictor. Five PRIGs and risk score were significantly associated with the clinical stage of CRC and five immune cells related to the risk score. Conclusion: The risk score was an independent prognostic biomarker for CRC patients. The research excavated immune genes that were associated with survival and that could be potential biomarkers for prognosis and treatment for CRC patients.

Translation regulatory long non-coding RNA 1 represents a potential prognostic biomarker for colorectal cancer

Long non-coding RNAs (lncRNAs) have attracted a lot of attention for their role in the development, progression and prognosis of colorectal cancer (CRC). However, little is known on the clinical significance of the translation regulatory lncRNA 1 (TRERNA1) in CRC. The present study aimed to explore the clinical value of TRERNA1 in patients with CRC. A total of 89 cancer-associated lncRNA genes were analyzed using the RT² lncRNA PCR array Human Cancer PathwayFinder. Following the PCR array, reverse transcription-quantitative (RT-q)PCR was conducted to identify the differential expression of TRERNA1 between 130 CRC and corresponding non-tumorous adjacent tissues. Additionally, the association between TRERNA1 expression and clinical characteristics was analyzed. Furthermore, TRERNA1 expression was knocked down via small interfering RNAs. The results of the PCR array and RT-qPCR revealed that TRERNA1 expression was significantly upregulated in CRC tissues compared with in adjacent normal tissues. TRERNA1 upregulation was positively associated with distant metastasis, perineural invasion, TNM stage, node metastasis stage and tumor diameter. Multivariate analysis revealed that patients with higher TRERNA1 expression had a shorter overall survival (OS) time and a less favorable prognosis compared with those in the low TRERNA1 expression group. Knockdown of TRERNA1 inhibited invasion and metastasis of CRC cells via regulating Snail expression. In conclusion, TRERNA1 expression was upregulated in CRC tissues. High expression levels of TRERNA1 may be associated with poor OS times, a less favorable prognosis and lymph node metastasis in patients with CRC. TRERNA1 may therefore serve as a useful and novel biomarker for CRC lymph node metastasis and prognosis.

Recent Electrochemical/Electrical Microfabricated Sensor Devices for Ionic and Polyionic Analytes

The recent technological advances combined with the development of new concepts and strategies have revolutionized the field of sensor devices, allowing access to increasingly sophisticated device structures associated with high sensitivities and selectivities. Among them, electrochemical and electrical sensors have gained the most interest because they offer unique intrinsic characteristics and meet the requirements to be integrated in more sophisticated devices including microfluidics or lab-on-chips, opening access to multiplex and all-in-one detection devices. In the present article, we outline and provide a short and concise overview on the most recent achievements in the field of electrical detection of ionic species as they display versatile roles in many important biological events and are ubiquitous in environment.

Prognostic implications of metabolism-associated gene signatures in colorectal cancer

Colorectal cancer (CRC) is one of the most common and deadly malignancies. Novel biomarkers for the diagnosis and prognosis of this disease must be identified. Besides, metabolism plays an essential role in the occurrence and development of CRC. This article aims to identify some critical prognosis-related metabolic genes (PRMGs) and construct a prognosis model of CRC patients for clinical use. We obtained the expression profiles of CRC from The Cancer Genome Atlas database (TCGA), then identified differentially expressed PRMGs by R and Perl software. Hub genes were filtered out by univariate Cox analysis and least absolute shrinkage and selection operator Cox analysis. We used functional enrichment analysis methods, such as Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis, to identify involved signaling pathways of PRMGs. The nomogram predicted overall survival (OS). Calibration traces were used to evaluate the consistency between the actual and the predicted survival rate. Finally, a prognostic model was constructed based on six metabolic genes (NAT2, XDH, GPX3, AKR1C4, SPHK1, and ADCY5), and the risk score was an independent prognostic prognosticator. Genetic expression and risk score were significantly correlated with clinicopathologic characteristics of CRC. A nomogram based on the clinicopathological feature of CRC and risk score accurately predicted the OS of individual CRC cancer patients. We also validated the results in the independent colorectal cancer cohorts GSE39582 and GSE87211. Our study demonstrates that the risk score is an independent prognostic biomarker and is closely correlated with the malignant clinicopathological characteristics of CRC patients. We also determined some metabolic genes associated with the survival and clinical stage of CRC as potential biomarkers for CRC diagnosis and treatment.