Changing Metabolic Patterns along the Colorectal Adenoma–Carcinoma Sequence

The Utility of Lipidomic Analysis in Colorectal Cancer Diagnosis and Prognosis-A Systematic Review of Recent Literature

Colorectal cancer (CRC) is among the most prevalent and lethal malignancies. Lipidomic investigations have revealed numerous disruptions in lipid profiles across various cancers. Studies on CRC exhibit potential for identifying novel diagnostic or prognostic indicators through lipidomic signatures. This review examines recent literature regarding lipidomic markers for CRC. PubMed database was searched for eligible articles concerning lipidomic biomarkers of CRC. After selection, 36 articles were included in the review. Several studies endeavor to establish sets of lipid biomarkers that demonstrate promising potential to diagnose CRC based on blood samples. Phosphatidylcholine, phosphatidylethanolamine, ceramides, and triacylglycerols (TAGs) appear to offer the highest diagnostic accuracy. In tissues, lysophospholipids, ceramides, and TAGs were among the most altered lipids, while unsaturated fatty acids also emerged as potential biomarkers. In-depth analysis requires both cell culture and animal studies. CRC involves multiple lipid metabolism alterations. Although numerous lipid species have been suggested as potential diagnostic markers, the establishment of standardized methods and the conduct of large-scale studies are necessary to facilitate their clinical application.

Metabolomics for searching validated biomarkers in cancer studies: a decade in review

INTRODUCTION

In the dynamic landscape of modern healthcare, the ability to anticipate and diagnose diseases, particularly in cases where early treatment significantly impacts outcomes, is paramount. Cancer, a complex and heterogeneous disease, underscores the critical importance of early diagnosis for patient survival. The integration of metabolomics information has emerged as a crucial tool, complementing the genotype-phenotype landscape and providing insights into active metabolic mechanisms and disease-induced dysregulated pathways.

AREAS COVERED

This review explores a decade of developments in the search for biomarkers validated within the realm of cancer studies. By critically assessing a diverse array of research articles, clinical trials, and studies, this review aims to present an overview of the methodologies employed and the progress achieved in identifying and validating biomarkers in metabolomics results for various cancer types.

EXPERT OPINION

Through an exploration of more than 800 studies, this review has allowed to establish a general idea about state-of-art in the search of biomarkers in metabolomics studies involving cancer which include certain level of results validation. The potential for metabolites as diagnostic markers to reach the clinic and make a real difference in patient health is substantial, but challenges remain to be explored.

Bidirectional effects of the tryptophan metabolite indole-3-acetaldehyde on colorectal cancer

BACKGROUND

Colorectal cancer (CRC) has a high incidence and mortality. Recent studies have shown that indole derivatives involved in gut microbiota metabolism can impact the tumorigenesis, progression, and metastasis of CRC.

AIM

To investigate the effect of indole-3-acetaldehyde (IAAD) on CRC.

METHODS

The effect of IAAD was evaluated in a syngeneic mouse model of CRC and CRC cell lines (HCT116 and DLD-1). Cell proliferation was assessed by Ki-67 fluorescence staining and cytotoxicity tests. Cell apoptosis was analysed by flow cytometry after staining with Annexin V-fluorescein isothiocyanate and propidium iodide. Invasiveness was investigated using the transwell assay. Western blotting and real-time fluorescence quantitative polymerase chain reaction were performed to evaluate the expression of epithelial-mesenchymal transition related genes and aryl hydrocarbon receptor (AhR) downstream genes. The PharmMapper, SEA, and SWISS databases were used to screen for potential target proteins of IAAD, and the core proteins were identified through the String database.

RESULTS

IAAD reduced tumorigenesis in a syngeneic mouse model. In CRC cell lines HCT116 and DLD1, IAAD exhibited cytotoxicity starting at 24 h of treatment, while it reduced Ki67 expression in the nucleus. The results of flow cytometry showed that IAAD induced apoptosis in HCT116 cells but had no effect on DLD1 cells, which may be related to the activation of AhR. IAAD can also increase the invasiveness and epithelial-mesenchymal transition of HCT116 and DLD1 cells. At low concentrations (< 12.5 μmol/L), IAAD only exhibited cytotoxic effects without promoting cell invasion. In addition, predictions based on online databases, protein-protein interaction analysis, and molecular docking showed that IAAD can bind to matrix metalloproteinase-9 (MMP9), angiotensin converting enzyme (ACE), poly(ADP-ribose) polymerase-1 (PARP1), matrix metalloproteinase-2 (MMP2), and myeloperoxidase (MPO).

CONCLUSION

Indole-3-aldehyde can induce cell apoptosis and inhibit cell proliferation to prevent the occurrence of CRC; however, at high concentrations (≥ 25 μmol/L), it can also promote epithelial-mesenchymal transition and invasion in CRC cells. IAAD activates AhR and directly binds MMP9, ACE, PARP1, MMP2, and MPO, which partly reveals why it has a bidirectional effect.

Bidirectional effects of the tryptophan metabolite indole-3-acetaldehyde on colorectal cancer

BACKGROUND

Colorectal cancer (CRC) has a high incidence and mortality. Recent studies have shown that indole derivatives involved in gut microbiota metabolism can impact the tumorigenesis, progression, and metastasis of CRC.

AIM

To investigate the effect of indole-3-acetaldehyde (IAAD) on CRC.

METHODS

The effect of IAAD was evaluated in a syngeneic mouse model of CRC and CRC cell lines (HCT116 and DLD-1). Cell proliferation was assessed by Ki-67 fluorescence staining and cytotoxicity tests. Cell apoptosis was analysed by flow cytometry after staining with Annexin V-fluorescein isothiocyanate and propidium iodide. Invasiveness was investigated using the transwell assay. Western blotting and real-time fluorescence quantitative polymerase chain reaction were performed to evaluate the expression of epithelial-mesenchymal transition related genes and aryl hydrocarbon receptor (AhR) downstream genes. The PharmMapper, SEA, and SWISS databases were used to screen for potential target proteins of IAAD, and the core proteins were identified through the String database.

RESULTS

IAAD reduced tumorigenesis in a syngeneic mouse model. In CRC cell lines HCT116 and DLD1, IAAD exhibited cytotoxicity starting at 24 h of treatment, while it reduced Ki67 expression in the nucleus. The results of flow cytometry showed that IAAD induced apoptosis in HCT116 cells but had no effect on DLD1 cells, which may be related to the activation of AhR. IAAD can also increase the invasiveness and epithelial-mesenchymal transition of HCT116 and DLD1 cells. At low concentrations (< 12.5 μmol/L), IAAD only exhibited cytotoxic effects without promoting cell invasion. In addition, predictions based on online databases, protein-protein interaction analysis, and molecular docking showed that IAAD can bind to matrix metalloproteinase-9 (MMP9), angiotensin converting enzyme (ACE), poly(ADP-ribose) polymerase-1 (PARP1), matrix metalloproteinase-2 (MMP2), and myeloperoxidase (MPO).

CONCLUSION

Indole-3-aldehyde can induce cell apoptosis and inhibit cell proliferation to prevent the occurrence of CRC; however, at high concentrations (≥ 25 μmol/L), it can also promote epithelial-mesenchymal transition and invasion in CRC cells. IAAD activates AhR and directly binds MMP9, ACE, PARP1, MMP2, and MPO, which partly reveals why it has a bidirectional effect.

Indole metabolites and colorectal cancer: Gut microbial tryptophan metabolism, host gut microbiome biomarkers, and potential intervention mechanisms

Tryptophan (Trp) functions in host-disease interactions. Its metabolism is a multi-pathway process. Indole and its derivatives are Trp metabolites unique to the human gut microbiota. Changes in Trp metabolism have also been detected in colorectal cancer (CRC). Here, combined with the existing CRC biomarkers, we ascribed it to the altered bacteria having the indole-producing ability by making a genomic prediction. We also reviewed the anti-inflammatory and possible anti-cancer mechanisms of indoles, including their effects on tumor cells, the ability to repair the gut barrier, regulation of the host immune system, and provide resistance against oxidative stress. Indole and its derivatives, along with related bacteria, could be targeted as auxiliary strategies to restrain cancer development in the future.

Gender-specific dysregulations of nondifferentially expressed biomarkers of metastatic colon cancer

Colon cancer is a common cancer type in both sexes and its mortality rate increases at the metastatic stage. Most studies exclude nondifferentially expressed genes from biomarker analysis of metastatic colon cancers. The motivation of this study is to find the latent associations of the nondifferentially expressed genes with metastatic colon cancers and to evaluate the gender specificity of such associations. This study formulates the expression level prediction of a gene as a regression model trained for primary colon cancers. The difference between a gene's predicted and original expression levels in a testing sample is defined as its mqTrans value (model-based quantitative measure of transcription regulation), which quantitatively measures the change of the gene's transcription regulation in this testing sample. We use the mqTrans analysis to detect the messenger RNA (mRNA) genes with nondifferential expression on their original expression levels but differentially expressed mqTrans values between primary and metastatic colon cancers. These genes are referred to as dark biomarkers of metastatic colon cancer. All dark biomarker genes were verified by two transcriptome profiling technologies, RNA-seq and microarray. The mqTrans analysis of a mixed cohort of both sexes could not recover gender-specific dark biomarkers. Most dark biomarkers overlap with long non-coding RNAs (lncRNAs), and these lncRNAs might have contributed their transcripts to calculating the dark biomarkers' expression levels. Therefore, mqTrans analysis serves as a complementary approach to identify dark biomarkers generally ignored by conventional studies, and it is essential to separate the female and male samples into two analysis experiments. The dataset and mqTrans analysis code are available at https://figshare.com/articles/dataset/22250536.

Integrated analysis of multi-omics data for the discovery of biomarkers and therapeutic targets for colorectal cancer

The considerable burden of colorectal cancer and the rising trend in young adults emphasize the necessity of understanding its underlying mechanisms, providing new diagnostic and prognostic markers, and improving therapeutic approaches. Precision medicine is a new trend all over the world and identification of novel biomarkers and therapeutic targets is a step forward towards this trend. In this context, multi-omics data and integrated analysis are being investigated to develop personalized medicine in the management of colorectal cancer. Given the large amount of data from multi-omics approach, data integration and analysis is a great challenge. In this Review, we summarize how statistical and machine learning techniques are applied to analyze multi-omics data and how it contributes to the discovery of useful diagnostic and prognostic biomarkers and therapeutic targets. Moreover, we discuss the importance of these biomarkers and therapeutic targets in the clinical management of colorectal cancer in the future. Taken together, integrated analysis of multi-omics data has great potential for finding novel diagnostic and prognostic biomarkers and therapeutic targets, however, there are still challenges to overcome in future studies.

Multi-Omics Approaches in Colorectal Cancer Screening and Diagnosis, Recent Updates and Future Perspectives

Colorectal cancer (CRC) is common Cancer as well as the third leading cause of mortality around the world; its exact molecular mechanism remains elusive. Although CRC risk is significantly correlated with genetic factors, the pathophysiology of CRC is also influenced by external and internal exposures and their interactions with genetic factors. The field of CRC research has recently benefited from significant advances through Omics technologies for screening biomarkers, including genes, transcripts, proteins, metabolites, microbiome, and lipidome unbiasedly. A promising application of omics technologies could enable new biomarkers to be found for the screening and diagnosis of CRC. Single-omics technologies cannot fully understand the molecular mechanisms of CRC. Therefore, this review article aims to summarize the multi-omics studies of Colorectal cancer, including genomics, transcriptomics, proteomics, microbiomics, metabolomics, and lipidomics that may shed new light on the discovery of novel biomarkers. It can contribute to identifying and validating new CRC biomarkers and better understanding colorectal carcinogenesis. Discovering biomarkers through multi-omics technologies could be difficult but valuable for disease genotyping and phenotyping. That can provide a better knowledge of CRC prognosis, diagnosis, and treatments.

Intravoxel incoherent motion DWI with different mathematical models in predicting rectal adenoma with and without canceration

PURPOSE

To evaluate the clinical value of intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) with mono-exponential (ME), bi-exponential (BE), and stretched-exponential (SE) models for predicting rectal adenomas with canceration.

MATERIAL AND METHODS

Sixty patients with postoperative pathology-confirmed rectal adenoma (n = 31) and adenoma with canceration (n = 29) were enrolled and underwent IVIM-DWI scanning. The ME-derived apparent diffusion coefficient (ADC), BE-derived true diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), SE-derived distributed diffusion coefficient (DDC), and water molecular diffusion heterogeneity index (α) were measured. The differences in each parameter between adenoma and canceration were compared. Multivariate binary logistic regression analysis was used to establish models for predicting rectal adenomas with canceration. Receiver operating characteristic curve analysis was applied to evaluate diagnostic performances of each model in terms of sensitivity, specificity, accuracy, and area under the curve (AUC).

RESULTS

The AUCs of ADC, D, D*, f, DDC and α were 0.851 (95 % confidence interval, CI, 0.735-0.930), 0.895 (95 % CI, 0.789-0.960), 0.720 (95 % CI, 0.589-0.828), 0.791 (95 % CI, 0.667-0.886), 0.841 (95 % CI, 0.724-0.923) and 0.738 (95 % CI, 0.608-0.834), respectively. The AUCs of BE and SE models were 0.927 (95 % CI, 0.829-0.978) and 0.874 (95 % CI, 0.763-0.946), respectively. The AUC, sensitivity, specificity, and accuracy of the derived four values (ADC, D, f, and DDC) from the combination of three models were 0.950, 96.6 % (95 % CI, 95.3-97.6 %), 80.6 % (95 % CI, 78.0-82.9 %), and 88.3 % (95 % CI, 86.2-90.2 %), respectively.

CONCLUSION

ADC can easily and effectively predict rectal adenomas with canceration. The BE model has a better combination of sensitivity and specificity for the diagnosis of rectal adenoma canceration.

UHPLC-HRMS–based serum lipisdomics reveals novel biomarkers to assist in the discrimination between colorectal adenoma and cancer

The development of a colorectal adenoma (CA) into carcinoma (CRC) is a long and stealthy process. There remains a lack of reliable biomarkers to distinguish CA from CRC. To effectively explore underlying molecular mechanisms and identify novel lipid biomarkers promising for early diagnosis of CRC, an ultrahigh-performance liquid chromatography tandem high-resolution mass spectrometry (UHPLC-HRMS) method was employed to comprehensively measure lipid species in human serum samples of patients with CA and CRC. Results showed significant differences in serum lipid profiles between CA and CRC groups, and 85 differential lipid species (P &lt; 0.05 and fold change &gt; 1.50 or &lt; 0.67) were discovered. These significantly altered lipid species were mainly involved in fatty acid (FA), phosphatidylcholine (PC), and triacylglycerol (TAG) metabolism with the constituent ratio &gt; 63.50%. After performance evaluation by the receiver operating characteristic (ROC) curve analysis, seven lipid species were ultimately proposed as potential biomarkers with the area under the curve (AUC) &gt; 0.800. Of particular value, a lipid panel containing docosanamide, SM d36:0, PC 36:1e, and triheptanoin was selected as a composite candidate biomarker with excellent performance (AUC = 0.971), and the highest selected frequency to distinguish patients with CA from patients with CRC based on the support vector machine (SVM) classification model. To our knowledge, this study was the first to undertake a lipidomics profile using serum intended to identify screening lipid biomarkers to discriminate between CA and CRC. The lipid panel could potentially serve as a composite biomarker aiding the early diagnosis of CRC. Metabolic dysregulation of FAs, PCs, and TAGs seems likely involved in malignant transformation of CA, which hopefully will provide new clues to understand its underlying mechanism.

Salivary metabolomics with machine learning for colorectal cancer detection

As the worldwide prevalence of colorectal cancer (CRC) increases, it is vital to reduce its morbidity and mortality through early detection. Saliva‐based tests are an ideal noninvasive tool for CRC detection. Here, we explored and validated salivary biomarkers to distinguish patients with CRC from those with adenoma (AD) and healthy controls (HC). Saliva samples were collected from patients with CRC, AD, and HC. Untargeted salivary hydrophilic metabolite profiling was conducted using capillary electrophoresis–mass spectrometry and liquid chromatography–mass spectrometry. An alternative decision tree (ADTree)‐based machine learning (ML) method was used to assess the discrimination abilities of the quantified metabolites. A total of 2602 unstimulated saliva samples were collected from subjects with CRC (n = 235), AD (n = 50), and HC (n = 2317). Data were randomly divided into training (n = 1301) and validation datasets (n = 1301). The clustering analysis showed a clear consistency of aberrant metabolites between the two groups. The ADTree model was optimized through cross‐validation (CV) using the training dataset, and the developed model was validated using the validation dataset. The model discriminating CRC + AD from HC showed area under the receiver‐operating characteristic curves (AUC) of 0.860 (95% confidence interval [CI]: 0.828‐0.891) for CV and 0.870 (95% CI: 0.837‐0.903) for the validation dataset. The other model discriminating CRC from AD + HC showed an AUC of 0.879 (95% CI: 0.851‐0.907) and 0.870 (95% CI: 0.838‐0.902), respectively. Salivary metabolomics combined with ML demonstrated high accuracy and versatility in detecting CRC.

Lipid Metabolism Interplay in CRC—An Update

Colorectal cancer (CRC) to date still ranks as one of the deadliest cancer entities globally, and despite recent advances, the incidence in young adolescents is dramatically increasing. Lipid metabolism has recently received increased attention as a crucial element for multiple aspects of carcinogenesis and our knowledge of the underlying mechanisms is steadily growing. However, the mechanism how fatty acid metabolism contributes to CRC is still not understood in detail. In this review, we aim to summarize our vastly growing comprehension and the accompanied complexity of cellular fatty acid metabolism in CRC by describing inputs and outputs of intracellular free fatty acid pools and how these contribute to cancer initiation, disease progression and metastasis. We highlight how different lipid pathways can contribute to the aggressiveness of tumors and affect the prognosis of patients. Furthermore, we focus on the role of lipid metabolism in cell communication and interplay within the tumor microenvironment (TME) and beyond. Understanding these interactions in depth might lead to the discovery of novel markers and new therapeutic interventions for CRC. Finally, we discuss the crucial role of fatty acid metabolism as new targetable gatekeeper in colorectal cancer.