Omics technologies for improved diagnosis and treatment of colorectal cancer: Technical advancement and major perspectives

Precision medicine in colorectal cancer: genomics profiling and targeted treatment

Precision medicine has revolutionized the treatment of colorectal cancer by enabling a personalized approach tailored to each patient's unique genetic characteristics. Genomic profiling allows for the identification of specific mutations in genes such as KRAS, BRAF, and PIK3CA, which play a crucial role in cell signaling pathways that regulate cell proliferation, apoptosis, and differentiation. This information enables doctors to select targeted therapies that inhibit specific molecular pathways, maximizing treatment effectiveness and minimizing side effects. Precision medicine also facilitates adaptive monitoring of tumor progression, allowing for adjustments in therapy to maintain treatment effectiveness. While challenges such as high costs, limited access to genomic technology, and the need for more representative genomic data for diverse populations remain, collaboration between researchers, medical practitioners, policymakers, and the pharmaceutical industry is crucial to ensure that precision medicine becomes a standard of care accessible to all. With continued advances and support, precision medicine has the potential to improve treatment outcomes, reduce morbidity and mortality rates, and enhance the quality of life for colorectal cancer patients worldwide.

Emerging Nanoparticle-Based Diagnostics and Therapeutics for Cancer: Innovations and Challenges

Malignant growth is expected to surpass other significant causes of death as one of the top reasons for dismalness and mortality worldwide. According to a World Health Organization (WHO) study, this illness causes approximately between 9 and 10 million instances of deaths annually. Chemotherapy, radiation, and surgery are the three main methods of treating cancer. These methods seek to completely eradicate all cancer cells while having the fewest possible unintended impacts on healthy cell types. Owing to the lack of target selectivity, the majority of medications have substantial side effects. On the other hand, nanomaterials have transformed the identification, diagnosis, and management of cancer. Nanostructures with biomimetic properties have been grown as of late, fully intent on observing and treating the sickness. These nanostructures are expected to be consumed by growth in areas with profound disease. Furthermore, because of their extraordinary physicochemical properties, which incorporate nanoscale aspects, a more prominent surface region, explicit geometrical features, and the ability to embody different substances within or on their outside surfaces, nanostructures are remarkable nano-vehicles for conveying restorative specialists to their designated regions. This review discusses recent developments in nanostructured materials such as graphene, dendrimers, cell-penetrating peptide nanoparticles, nanoliposomes, lipid nanoparticles, magnetic nanoparticles, and nano-omics in the diagnosis and management of cancer.

Machine learning-based identification of proteomic markers in colorectal cancer using UK Biobank data

Colorectal cancer is one of the leading causes of cancer-related mortality in the world. Incidence and mortality are predicted to rise globally during the next several decades. When detected early, colorectal cancer is treatable with surgery and medications. This leads to the requirement for prognostic and diagnostic biomarker development. Our study integrates machine learning models and protein network analysis to identify protein biomarkers for colorectal cancer. Our methodology leverages an extensive collection of proteome profiles from both healthy and colorectal cancer individuals. To identify a potential biomarker with high predictive ability, we used three machine learning models. To enhance the interpretability of our models, we quantify each protein's contribution to the model's predictions using SHapley Additive exPlanations values. Three classifiers-LASSO, XGBoost, and LightGBM were evaluated for predictive performance along with hyperparameter tuning of each model using grid search, with LASSO achieving the highest AUC of 75% in the UK Biobank dataset and the AUCs for LightGBM and XGBoost are 69.61% and 71.42%, respectively. Using SHapley Additive exPlanations values, TFF3, LCN2, and CEACAM5 were found to be key biomarkers associated with cell adhesion and inflammation. Protein quantitative trait loci analyze studies provided further evidence for the involvement of TFF1, CEACAM5, and SELE in colorectal cancer, with possible connections to the PI3K/Akt and MAPK signaling pathways. By offering insights into colorectal cancer diagnostics and targeted therapeutics, our findings set the stage for further biomarker validation.

Innovative Bacterial Therapies and Genetic Engineering Approaches in Colorectal Cancer: A Review of Emerging Strategies and Clinical Implications

Colorectal cancer (CRC) is considered a widespread cancer, ranking second in mortality and incidence among cancer patients worldwide. CRC develops from adenoma to carcinoma through the dynamic interplay of genetic and environmental factors. The conventional modes of treatment, including operation, chemotherapy, and irradiation, are associated with significant challenges, such as drug resistance and toxicity, necessitating the exploration of new treatment modalities. These difficulties reveal the necessity of the emergence of new therapeutic approaches. This review mainly emphasizes the bacterial-based therapies that have recently developed like the engineered bacteriophage therapy and bacterial immunotherapy that pale the existing chemotherapy in terms of toxicity but are effective in killing tumor cells. Also, it also investigates various molecular genetic engineering strategies such as CRISPR-Cas9, CRISPR prime editing and gene silencing to achieve better targeting of CRC. Implementing these new approaches into the forefront of CRC treatment may bring better, more effective therapy with fewer side effects on patients' quality of life.

Current status of serum metabolites biomarkers for polyps and colorectal cancer: a systematic review

Background

Early detection of colorectal cancer (CRC) is crucial to enhance the disease treatment and prognosis of patients. Colonoscopy remains the gold standard for CRC detection; however, it requires trained personnel with expensive tools. Currently, serum metabolites have been discovered to be used to discriminate patients with polyps and CRC. This study aimed to identify the most commonly detected predictive serum metabolites for polyps and CRC.

Methods

A systematic search of the Web of Science, PubMed, and Cochrane Library databases was conducted using PRISMA guidelines. Ten studies investigating serum metabolite biomarkers of CRC and polyps using different analytical platforms and study populations were included. QUADOMICS tool was used to analyse the quality of the included studies. All reported metabolites were then enriched into the pathways using MetaboAnalyst 5.0.

Results

We found that several potential signature metabolites overlapped between studies, including tyrosine, lysine, cystine, arabinose, and lactate for CRC and lactate and glutamate for polyps. The most affected pathways related to CRC were the urea cycle, glutathione metabolism, purine metabolism, glutamate metabolism, and ammonia recycling. In contrast, those affected in the polyps were the urea cycle, glutamate metabolism, glutathione metabolism, arginine and proline metabolism, and carnitine synthesis.

Conclusions

This review has found commonly detected serum metabolites for polyps and CRC with huge potential to be used in clinical settings. However, the differences between altered pathways in polyps and CRC, other external factors, and their effects on the regulation level, sensitivity, and specificity of each identified metabolite remained unclear, which could benefit from a further extensive cohort study and well-defined analysis equipment.

Exploring prognostic biomarkers in pathological images of colorectal cancer patients via deep learning

Hematoxylin and eosin (H&E) whole slide images provide valuable information for predicting prognostic outcomes in colorectal cancer (CRC) patients. However, extracting prognostic indicators from pathological images is challenging due to the subtle complexities of phenotypic information. We trained a weakly supervised deep learning model on data from 640 CRC patients in the prostate, lung, colorectal, and ovarian (PLCO) cancer screening trial dataset and validated it using data from 522 CRC patients in the cancer genome atlas (TCGA) dataset. We created the colorectal cancer risk score (CRCRS) to assess patient prognosis, visualized the pathological phenotype of the risk score using Grad-CAM, and employed multiomics data from the TCGA CRC cohort to investigate the potential biological mechanisms underlying the risk score. The overall survival analysis revealed that the CRCRS served as an independent prognostic indicator for both the PLCO cohort (p < 0.001) and the TCGA cohort (p < 0.001), with its predictive efficacy remaining unaffected by the clinical staging system. Additionally, satisfactory chemotherapeutic benefits were observed in stage II/III CRC patients with high CRCRS but not in those with low CRCRS. A pathomics nomogram constructed by integrating the CRCRS with the tumor-node-metastasis (TNM) staging system enhanced prognostic prediction accuracy compared with using the TNM staging system alone. Noteworthy features of the risk score were identified, such as immature tumor mesenchyme, disorganized gland structures, small clusters of cancer cells associated with unfavorable prognosis, and infiltrating inflammatory cells associated with favorable prognosis. The TCGA multiomics data revealed potential correlations between the CRCRS and the activation of energy production and metabolic pathways, the tumor immune microenvironment, and genetic mutations in APC, SMAD2, EEF1AKMT4, EPG5, and TANC1. In summary, our deep learning algorithm identified the CRCRS as a prognostic indicator in CRC, providing a significant approach for prognostic risk stratification and tailoring precise treatment strategies for individual patients.

Genetics, diet, microbiota, and metabolome: partners in crime for colon carcinogenesis

Colorectal cancer (CRC) ranks among the most prevalent malignant tumors worldwide, with a multifactorial etiology encompassing genetic, environmental, and life-style factors, as well as the intestinal microbiota and its metabolome. These risk factors often work together in specific groups of patients, influencing how CRC develops and progresses. Importantly, alterations in the gut microbiota act as a critical nexus in this interplay, significantly affecting susceptibility to CRC. This review highlights recent insights into unmodifiable and modifiable risk factors for CRC and how they might interact with the gut microbiota and its metabolome. Understanding the mechanisms of these interactions will help us develop targeted, precision-medicine strategies that can adjust the composition of the gut microbiota to meet individual health needs, preventing or treating CRC more effectively.

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.

A meta-analysis of bulk RNA-seq datasets identifies potential biomarkers and repurposable therapeutics against Alzheimer's disease

Alzheimer's disease (AD) poses a major challenge due to its impact on the elderly population and the lack of effective early diagnosis and treatment options. In an effort to address this issue, a study focused on identifying potential biomarkers and therapeutic agents for AD was carried out. Using RNA-Seq data from AD patients and healthy individuals, 12 differentially expressed genes (DEGs) were identified, with 9 expressing upregulation (ISG15, HRNR, MTATP8P1, MTCO3P12, DTHD1, DCX, ST8SIA2, NNAT, and PCDH11Y) and 3 expressing downregulation (LTF, XIST, and TTR). Among them, TTR exhibited the lowest gene expression profile. Interestingly, functional analysis tied TTR to amyloid fiber formation and neutrophil degranulation through enrichment analysis. These findings suggested the potential of TTR as a diagnostic biomarker for AD. Additionally, druggability analysis revealed that the FDA-approved drug Levothyroxine might be effective against the Transthyretin protein encoded by the TTR gene. Molecular docking and dynamics simulation studies of Levothyroxine and Transthyretin suggested that this drug could be repurposed to treat AD. However, additional studies using in vitro and in vivo models are necessary before these findings can be applied in clinical applications.

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.

Machine Learning-Based Integrated Multiomics Characterization of Colorectal Cancer Reveals Distinctive Metabolic Signatures

The metabolic signature identification of colorectal cancer is critical for its early diagnosis and therapeutic approaches that will significantly block cancer progression and improve patient survival. Here, we combined an untargeted metabolic analysis strategy based on internal extractive electrospray ionization mass spectrometry and the machine learning approach to analyze metabolites in 173 pairs of cancer samples and matched normal tissue samples to build robust metabolic signature models for diagnostic purposes. Screening and independent validation of metabolic signatures from colorectal cancers via machine learning methods (Logistic Regression_L1 for feature selection and eXtreme Gradient Boosting for classification) was performed to generate a panel of seven signatures with good diagnostic performance (the accuracy of 87.74%, sensitivity of 85.82%, and specificity of 89.66%). Moreover, seven signatures were evaluated according to their ability to distinguish between cancer and normal tissues, with the metabolic molecule PC (30:0) showing good diagnostic performance. In addition, genes associated with PC (30:0) were identified by multiomics analysis (combining metabolic data with transcriptomic data analysis) and our results showed that PC (30:0) could promote the proliferation of colorectal cancer cell SW480, revealing the correlation between genetic changes and metabolic dysregulation in cancer. Overall, our results reveal potential determinants affecting metabolite dysregulation, paving the way for a mechanistic understanding of altered tissue metabolites in colorectal cancer and design interventions for manipulating the levels of circulating metabolites.

A novel method for enrichment of Morganella morganii in fecal samples using designed culture medium

Morganella morganii is a gram negative, facultative anaerobic rod-shaped bacterium, commonly found in environment and in the intestine of human, mammals, and reptiles as a part of their gut microbiome. M. morganii can cause Gram-negative folliculitis, black nail infection, acute retiform purpura, fetal demise, and subdural empyema. The increasing frequency of M. morganii infections generate the need for efficient methods to enrich the presence of M. morganii in clinical samples to make its detection easier. Culturomics aims to grow and maximize the number of culturable bacteria. Different methods are followed to maximize the growth of minority population of bacteria by disrupting the growth of bacteria which are present in higher concentration. This article presents a method for selective enriching the M. morganii in human fecal samples. This method includes prior incubation of fecal microbiota in an anaerobic environment, adding supplement like fecal water to give dormant bacteria a break to become active to grow to threshold concentration, and an enrichment stage which provides the additional opportunity of growing to M. morganii on the selective medium. This method also provides an ingenuous way for augmenting the growth of fecal M. morganii species.

Beetroot extract@chitosan nanocomposite as a promising approach towards cancer therapy

The exceptional antioxidant properties of beetroot (BR) and the cancer antiproliferative effects of chitosan nanoparticles (CS NP) have led to the synthesis of a BR@CS nanocomposite (NC) in this study. The novel BR@CS NC was applied to human epithelial colorectal adenocarcinoma (Caco-2), human epithelial ductal breast carcinoma (T-47D), and human epithelial lung carcinoma (A549) cells. SEM characterization of CS NP revealed a variety of particle shapes ranging from 20 to 58 nm in diameter. UV-VIS analysis confirmed the formation of the BR@CS NC, while FTIR analysis demonstrated strong hydrogen bonds between CS NP and BR. These bonds reduced the positive surface charge of CS NP, as indicated by zeta potential analysis. When applied to cancer cell lines at a concentration of 250 μg/mL, the BR@CS NC successfully eradicated 89 % of A549, 88 % of T-47D, and 83 % of Caco-2 cell lines. The cell death mode exhibited extensive, apoptotic, and massive necrotic changes in all cell lines treated with BR@CS NC. Caspase 3 (CasP3) and P53 levels were elevated in BR@CS NC-treated cells. This study merges BR's antioxidant and anti-inflammatory properties with the antiangiogenic mechanism and inhibition of tumors by CS NP, resulting in a unique and innovative strategy for cancer treatment.

Profiling the metabolic disorder and detection of colorectal cancer based on targeted amino acids metabolomics

BACKGROUND

The morbidity of cancer keeps growing worldwide, and among that, the colorectal cancer (CRC) has jumped to third. Existing early screening tests for CRC are limited. The aim of this study was to develop a diagnostic strategy for CRC by plasma metabolomics.

METHODS

A targeted amino acids metabolomics method was developed to quantify 32 plasma amino acids in 130 CRC patients and 216 healthy volunteers, to identify potential biomarkers for CRC, and an independent sample cohort comprising 116 CRC subjects, 33 precancerosiss patients and 195 healthy volunteers was further used to validate the diagnostic model. Amino acids-related genes were retrieved from Gene Expression Omnibus and Molecular Signatures Database and analyzed.

RESULTS

Three were chosen out of the 32 plasma amino acids examined. The tryptophan / sarcosine / glutamic acid -based receiver operating characteristic (ROC) curve showed the area under the curve (AUC) of 0.955 (specificity 83.3% and sensitivity 96.8%) for all participants, and the logistic regression model were used to distinguish between early stage (I and II) of CRC and precancerosiss patients, which showed superiority to the commonly used carcinoembryonic antigen. The GO and KEGG enrichment analysis proved many alterations in amino acids metabolic pathways in tumorigenesis.

CONCLUSION

This altered plasma amino acid profile could effectively distinguish CRC patients from precancerosiss patients and healthy volunteers with high accuracy. Prognostic tests based on the tryptophan/sarcosine/glutamic acid biomarkers in the large population could assess the clinical significance of CRC early detection and intervention.

Proteomics-based identification of proteins in tumor-derived exosomes as candidate biomarkers for colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is the second leading cause of cancer-related death, with high morbidity worldwide. There is an urgent need to find reliable diagnostic biomarkers of CRC and explore the underlying molecular mechanisms. Exosomes are involved in intercellular communication and participate in multiple pathological processes, serving as an important part of the tumor microenvironment.

AIM

To investigate the proteomic characteristics of CRC tumor-derived exosomes and to identify candidate exosomal protein markers for CRC.

METHODS

In this study, 10 patients over 50 years old who were diagnosed with moderately differentiated adenocarcinoma were recruited. We paired CRC tissues and adjacent normal intestinal tissues (> 5 cm) to form the experimental and control groups. Purified exosomes were extracted separately from each tissue sample. Data-independent acquisition mass spectrometry was implemented in 8 matched samples of exosomes to explore the proteomic expression profiles, and differentially expressed proteins (DEPs) were screened by bioinformatics analysis. Promising exosomal proteins were verified using parallel reaction monitoring (PRM) analysis in 10 matched exosome samples.

RESULTS

A total of 1393 proteins were identified in the CRC tissue group, 1304 proteins were identified in the adjacent tissue group, and 283 proteins were significantly differentially expressed between them. Enrichment analysis revealed that DEPs were involved in multiple biological processes related to cytoskeleton construction, cell movement and migration, immune response, tumor growth and telomere metabolism, as well as ECM-receptor interaction, focal adhesion and mTOR signaling pathways. Six differentially expressed exosomal proteins (NHP2, OLFM4, TOP1, SAMP, TAGL and TRIM28) were validated by PRM analysis and evaluated by receiver operating characteristic curve (ROC) analysis. The area under the ROC curve was 0.93, 0.96, 0.97, 0.78, 0.75, and 0.88 (P < 0.05) for NHP2, OLFM4, TOP1, SAMP, TAGL, and TRIM28, respectively, indicating their good ability to distinguish CRC tissues from adjacent intestinal tissues.

CONCLUSION

In our study, comprehensive proteomic profiles were obtained for CRC tissue exosomes. Six exosomal proteins, NHP2, OLFM4, TOP1, SAMP, TAGL and TRIM28, may be promising diagnostic markers and effective therapeutic targets for CRC, but further experimental investigation is needed.

Distinct Signatures of Tumor-Associated Microbiota and Metabolome in Low-Grade vs. High-Grade Dysplastic Colon Polyps: Inference of Their Role in Tumor Initiation and Progression

According to the driver-passenger model for colorectal cancer (CRC), the tumor-associated microbiota is a dynamic ecosystem of bacterial species where bacteria with carcinogenic features linked to CRC initiation are defined as "drivers", while opportunistic bacteria colonizing more advanced tumor stages are known as "passengers". We reasoned that also gut microbiota-associated metabolites may be differentially enriched according to tumor stage, and be potential determinants of CRC development. Thus, we characterized the mucosa- and lumen-associated microbiota (MAM and LAM, respectively) and mucosa-associated metabolites in low- vs. high-grade dysplastic colon polyps from 78 patients. We show that MAM, obtained with a new biopsy-preserving approach, and LAM differ in composition and α/β-diversity. By stratifying patients for polyp histology, we found that bacteria proposed as passengers by previous studies colonized high-grade dysplastic adenomas, whereas driver taxa were enriched in low-grade polyps. Furthermore, we report altered "mucosa-associated metabolite" levels in low- vs. high-grade groups. Integrated microbiota-metabolome analysis suggests the involvement of the gut microbiota in the production and consumption of these metabolites. Altogether, our findings support the involvement of bacterial species and associated metabolites in CRC mucosal homeostasis in a tumor-stage-specific manner. These distinct signatures may be used to distinguish low-grade from high-grade dysplastic polyps.

Multi-omics insights into the interplay between gut microbiota and colorectal cancer in the "microworld" age

Colorectal cancer (CRC) is a multifactorial heterogeneous disease largely due to both genetic predisposition and environmental factors including the gut microbiota, a dynamic microbial ecosystem inhabiting the gastrointestinal tract. Elucidation of the molecular mechanisms by which the gut microbiota interacts with the host may contribute to the pathogenesis, diagnosis, and promotion of CRC. However, deciphering the influence of genetic variants and interactions with the gut microbial ecosystem is rather challenging. Despite recent advancements in single omics analysis, the application of multi-omics approaches to integrate multiple layers of information in the microbiome and host to introduce effective prevention, diagnosis, and treatment strategies is still in its infancy. Here, we integrate host- and microbe-based multi-omics studies, respectively, to provide a strategy to explore potential causal relationships between gut microbiota and colorectal cancer. Specifically, we summarize the recent multi-omics studies such as metagenomics combined with metabolomics and metagenomics combined with genomics. Meanwhile, the sample size and sample types commonly used in multi-omics research, as well as the methods of data analysis, were also generalized. We highlight multiple layers of information from multi-omics that need to be verified by different types of models. Together, this review provides new insights into the clinical diagnosis and treatment of colorectal cancer patients.

The Impact of Fluorination on the Design of Histone Deacetylase Inhibitors

In recent years, histone deacetylases (HDACs) have emerged as promising targets in the treatment of cancer. The approach is to inhibit HDACs with drugs known as HDAC inhibitors (HDACis). Such HDACis are broadly classified according to their chemical structure, e.g., hydroxamic acids, benzamides, thiols, short-chain fatty acids, and cyclic peptides. Fluorination plays an important role in the medicinal-chemical design of new active representatives. As a result of the introduction of fluorine into the chemical structure, parameters such as potency or selectivity towards isoforms of HDACs can be increased. However, the impact of fluorination cannot always be clearly deduced. Nevertheless, a change in lipophilicity and, hence, solubility, as well as permeability, can influence the potency. The selectivity towards certain HDACs isoforms can be explained by special interactions of fluorinated compounds with the structure of the slightly different enzymes. Another aspect is that for a more detailed investigation of newly synthesized fluorine-containing active compounds, fluorination is often used for the purpose of labeling. Aside from the isotope ¹⁹F, which can be detected by nuclear magnetic resonance spectroscopy, the positron emission tomography of ¹⁸F plays a major role. However, to our best knowledge, a survey of the general effects of fluorination on HDACis development is lacking in the literature to date. Therefore, the aim of this review is to highlight the introduction of fluorine in the course of chemical synthesis and the impact on biological activity, using selected examples of recently developed fluorinated HDACis.

Current Role of Modern Chromatography with Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy in the Investigation of Biomarkers of Endometriosis

Endometriosis has a wide range of clinical manifestations, and the disease course is unpredictable, making the diagnosis a challenging task. Despite significant advances in the pathophysiology of endometriosis and various proposed theories, the exact etiology is not fully understood and is still unknown. The most commonly used biomarker of endometriosis is CA-125, however, it is nonspecific and is applied for cancers diagnosis. Therefore, the development of reliable noninvasive diagnostic tests for the early diagnosis of endometriosis remains one of the top priorities. Omics technologies are very promising approaches for constructing diagnostic models and biomarker discovery. Their use can greatly facilitate the study of such a complex disease as endometriosis. Nowadays, powerful analytical platforms commonly used in omics, such as gas and liquid chromatography with mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, have proven to be a promising tools for biomarker discovery. The aim of this review is to summarize the various features of the analytical approaches, practical challenges and features of gas and liquid chromatography with MS and NMR spectroscopy (including sample processing protocols, technological advancements, and methodology) used for profiling of metabolites, lipids, peptides and proteins in physiological fluids and tissues from patients with endometriosis. In addition, this report devotes special attention to the issue of how comprehensive analyses of these profiles can effectively contribute to the study of endometriosis. The search query included reports published between 2012 and 2022 years in PubMed, Web-of-Science, SCOPUS, Science Direct.

Transcriptomic Signatures in Colorectal Cancer Progression

AIMS

Due to a large number of identified hub-genes encoding key molecular regulators, which are involved in signal transduction and metabolic pathways in cancers, it is relevant to systemize and update these findings.

BACKGROUND

Colorectal cancer (CRC) is the third leading cause of cancer death in the world, with high metastatic potential. Elucidating the pathogenic mechanisms and selection of novel biomarkers in CRC is of great clinical significance.

OBJECTIVE

This analytical review aims at the systematization of bioinformatics and experimental identification of hub-genes associated with CRC for a more consolidated understanding of common features in networks and pathways in CRC progression as well as hub-genes selection.

RESULTS

In total, 301 hub-genes were derived from 40 articles. The "core" consisted of 28 hub-genes (CCNB1, LPAR1, BGN, CXCL3, COL1A2, UBE2C, NMU, COL1A1, CXCL2, CXCL11, CDK1, TOP2A, AURKA, SST, CXCL5, MMP3, CCND1, TIMP1, CXCL8, CXCL1, CXCL12, MYC, CCNA2, GCG, GUCA2A, PAICS, PYY and THBS2) mentioned in not less than three articles and having clinical significance in cancerassociated pathways. Of them, there were two discrete clusters enriched in chemokine signaling and cell cycle regulatory genes. High expression levels of BGN and TIMP1 and low expression levels of CCNB1, CXCL3, CXCL2, CXCL2 and PAICS were associated with unfavorable overall survival of patients with CRC. Differently expressed genes such as LPAR1, SST, CXCL12, GUCA2A, and PYY were shown as down regulated, whereas BGN, CXCL3, UBE2C, NMU, CXCL11, CDK1, TOP2A, AURKA, MMP3, CCND1, CXCL1, MYC, CCNA2, PAICS were up regulated genes in CRC. It was also found that MMP3, THBS2, TIMP1 and CXCL12 genes were associated with metastatic CRC. Network analysis in ONCO.IO showed that upstream master regulators RELA, STAT3, SOX2, FOXM1, SMAD3 and NF-kB were connected with "core" hub-genes. Conclusión: Results obtained are of useful fundamental information on revealing the mechanism of pathogenicity, cellular target selection for optimization of therapeutic interventions, as well as transcriptomics prognostic and predictive biomarkers development.

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.

Detecting Colorectal Adenomas and Cancer Using Volatile Organic Compounds in Exhaled Breath: A Proof-of-Principle Study to Improve Screening

INTRODUCTION

Early detection of colorectal cancer (CRC) by screening programs is crucial because survival rates worsen at advanced stages. However, the currently used screening method, the fecal immunochemical test (FIT), suffers from a high number of false-positives and is insensitive for detecting advanced adenomas (AAs), resulting in false-negatives for these premalignant lesions. Therefore, more accurate, noninvasive screening tools are needed. In this study, the utility of analyzing volatile organic compounds (VOCs) in exhaled breath in a FIT-positive population to detect the presence of colorectal neoplasia was studied.

METHODS

In this multicenter prospective study, breath samples were collected from 382 FIT-positive patients with subsequent colonoscopy participating in the national Dutch bowel screening program (n = 84 negative controls, n = 130 non-AAs, n = 138 AAs, and n = 30 CRCs). Precolonoscopy exhaled VOCs were analyzed using thermal desorption-gas chromatography-mass spectrometry, and the data were preprocessed and analyzed using machine learning techniques.

RESULTS

Using 10 discriminatory VOCs, AAs could be distinguished from negative controls with a sensitivity and specificity of 79% and 70%, respectively. Based on this biomarker profile, CRC and AA combined could be discriminated from controls with a sensitivity and specificity of 77% and 70%, respectively, and CRC alone could be discriminated from controls with a sensitivity and specificity of 80% and 70%, respectively. Moreover, the feasibility to discriminate non-AAs from controls and AAs was shown.

DISCUSSION

VOCs in exhaled breath can detect the presence of AAs and CRC in a CRC screening population and may improve CRC screening in the future.

In Vitro Cytotoxicity Evaluation of Plastoquinone Analogues against Colorectal and Breast Cancers along with In Silico Insights

Colorectal cancer (CRC) and breast cancer are leading causes of death globally, due to significant challenges in detection and management. The late-stage diagnosis and treatment failures require the discovery of potential anticancer agents to achieve a satisfactory therapeutic effect. We have previously reported a series of plastoquinone analogues to understand their cytotoxic profile. Among these derivatives, three of them (AQ-11, AQ-12, and AQ-15) were selected by the National Cancer Institute (NCI) to evaluate their in vitro antiproliferative activity against a panel of 60 human tumor cell lines. AQ-12 exhibited significant antiproliferative activity against HCT-116 CRC and MCF-7 breast cancer cells at a single dose and further five doses. MTT assay was also performed for AQ-12 at different concentrations against these two cells, implying that AQ-12 exerted notable cytotoxicity toward HCT-116 (IC₅₀ = 5.11 ± 2.14 μM) and MCF-7 (IC₅₀ = 6.06 ± 3.09 μM) cells in comparison with cisplatin (IC₅₀ = 23.68 ± 6.81 μM and 19.67 ± 5.94 μM, respectively). This compound also augmented apoptosis in HCT-116 (62.30%) and MCF-7 (64.60%) cells comparable to cisplatin (67.30% and 78.80%, respectively). Molecular docking studies showed that AQ-12 bound to DNA, forming hydrogen bonding through the quinone scaffold. In silico pharmacokinetic determinants indicated that AQ-12 demonstrated drug-likeness with a remarkable pharmacokinetic profile for future mechanistic anti-CRC and anti-breast cancer activity studies.

CD8+ T cell-associated genes MS4A1 and TNFRSF17 are prognostic markers and inhibit the progression of colon cancer

Background

Colon cancer (CC) is among the top three diseases with the highest morbidity and mortality rates worldwide. Its increasing incidence imposes a major global health burden. Immune checkpoint inhibitors, such as anti-PD-1 and anti-PD-L1, can be used for the treatment of CC; however, most patients with CC are resistant to immunotherapy. Therefore, identification of biomarkers that can predict immunotherapy sensitivity is necessary for selecting patients with CC who are eligible for immunotherapy.

Methods

Differentially expressed genes associated with the high infiltration of CD8+ T cells were identified in CC and para-cancerous samples via bioinformatic analysis. Kaplan–Meier survival analysis revealed that MS4A1 and TNFRSF17 were associated with the overall survival of patients with CC. Cellular experiments were performed for verification, and the protein expression of target genes was determined via immunohistochemical staining of CC and the adjacent healthy tissues. The proliferation, migration and invasion abilities of CC cells with high expression of target genes were determined via in vitro experiments.

Results

Differential gene expression, weighted gene co-expression and survival analyses revealed that patients with CC with high expression of MS4A1 and TNFRSF17 had longer overall survival. The expression of these two genes was lower in CC tissues than in healthy colon tissues and was remarkably associated with the infiltration of various immune cells, including CD8+ T cells, in the tumour microenvironment (TME) of CC. Patients with CC with high expression of MS4A1 and TNFRSF17 were more sensitive to immunotherapy. Quantitative reverse transcription-polymerase chain reaction, western blotting and immunohistochemical staining validated the differential expression of MS4A1 and TNFRSF17. In addition, Cell Counting Kit-8, wound healing and transwell assays revealed that the proliferation, migration and invasion abilities of CC cells were weakened after overexpression of MS4A1 and TNFRSF17.

Conclusions

The core genes MS4A1 and TNFRSF17 can be used as markers to predict the sensitivity of patients with CC to immunotherapy and have potential applications in gene therapy to inhibit CC progression.

Asparagine, colorectal cancer, and the role of sex, genes, microbes, and diet: A narrative review

Asparagine (Asn) and enzymes that catalyze the metabolism of Asn have been linked to the regulation and propagation of colorectal cancer (CRC). Increased Asn and asparagine synthetase (ASNS) expression, both contribute to CRC progression and metastasis. In contradistinction, L-asparaginase (ASNase) which breaks down Asn, exhibits an anti-tumor effect. Metabolic pathways such as KRAS/PI3K/AKT/mTORC1 signaling and high SOX12 expression can positively regulate endogenous Asn production. Conversely, the tumor suppressor, TP53, negatively impacts ASNS, thus limiting Asn synthesis and reducing tumor burden. Asn abundance can be altered by factors extrinsic to the cancer cell such as diet, the microbiome, and therapeutic use of ASNase. Recent studies have shown that sex-related factors can also influence the regulation of Asn, and high Asn production results in poorer prognosis for female CRC patients but not males. In this narrative review, we critically review studies that have examined endogenous and exogenous modulators of Asn bioavailability and summarize the key metabolic networks that regulate Asn metabolism. We also provide new hypotheses regarding sex-related influences on Asn, including the involvement of the sex-steroid hormone estrogen and estrogen receptors. Further, we hypothesize that sex-specific factors that influence Asn metabolism can influence clinical outcomes in CRC patients.

Multiomics technologies: role in disease biomarker discoveries and therapeutics

 

Medical research has been revolutionized after the publication of the full human genome. This was the major landmark that paved the way for understanding the biological functions of different macro and micro molecules. With the advent of different high-throughput technologies, biomedical research was further revolutionized. These technologies constitute genomics, transcriptomics, proteomics, metabolomics, etc. Collectively, these high-throughputs are referred to as multi-omics technologies. In the biomedical field, these omics technologies act as efficient and effective tools for disease diagnosis, management, monitoring, treatment and discovery of certain novel disease biomarkers. Genotyping arrays and other transcriptomic studies have helped us to elucidate the gene expression patterns in different biological states, i.e. healthy and diseased states. Further omics technologies such as proteomics and metabolomics have an important role in predicting the role of different biological molecules in an organism. It is because of these high throughput omics technologies that we have been able to fully understand the role of different genes, proteins, metabolites and biological pathways in a diseased condition. To understand a complex biological process, it is important to apply an integrative approach that analyses the multi-omics data in order to highlight the possible interrelationships of the involved biomolecules and their functions. Furthermore, these omics technologies offer an important opportunity to understand the information that underlies disease. In the current review, we will discuss the importance of omics technologies as promising tools to understand the role of different biomolecules in diseases such as cancer, cardiovascular diseases, neurodegenerative diseases and diabetes.

SUMMARY POINTS

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.

Multi-OMICs data analysis identifies molecular features correlating with tumor immunity in colon cancer

BACKGROUND

There is a current need for new markers with higher sensitivity and specificity to predict immune status and optimize immunotherapy use in colon cancer.

OBJECTIVE

We aimed to investigate the multi-OMICs features associated with colon cancer immunity and response to immunotherapy.

METHODS

We evaluated the association of multi-OMICs data from three colon cancer datasets (TCGA, CPTAC2, and Samstein) with antitumor immune signatures (CD8+ T cell infiltration, immune cytolytic activity, and PD-L1 expression). Using the log-rank test and hierarchical clustering, we explored the association of various OMICs features with survival and immune status in colon cancer.

RESULTS

Two gene mutations (TERT and ERBB4) correlated with antitumor cytolytic activity found also correlated with improved survival in immunotherapy-treated colon cancers. Moreover, the expression of numerous genes was associated with antitumor immunity, including GBP1, GBP4, GBP5, NKG7, APOL3, IDO1, CCL5, and CXCL9. We clustered colon cancer samples into four immuno-distinct clusters based on the expression levels of 82 genes. We have also identified two proteins (PREX1 and RAD50), ten miRNAs (hsa-miR-140, 146, 150, 155, 342, 59, 342, 511, 592 and 1977), and five oncogenic pathways (CYCLIN, BCAT, CAMP, RB, NRL, EIF4E, and VEGF signaling pathways) significantly correlated with antitumor immune signatures.

CONCLUSION

These molecular features are potential markers of tumor immune status and response to immunotherapy.

Machine Learning-Based Identification of Colon Cancer Candidate Diagnostics Genes

Simple Summary

We developed a predictive approach using different machine learning methods to identify a number of genes that can potentially serve as novel diagnostic colon cancer biomarkers.

Abstract

Background: Colorectal cancer (CRC) is the third leading cause of cancer-related death and the fourth most commonly diagnosed cancer worldwide. Due to a lack of diagnostic biomarkers and understanding of the underlying molecular mechanisms, CRC’s mortality rate continues to grow. CRC occurrence and progression are dynamic processes. The expression levels of specific molecules vary at various stages of CRC, rendering its early detection and diagnosis challenging and the need for identifying accurate and meaningful CRC biomarkers more pressing. The advances in high-throughput sequencing technologies have been used to explore novel gene expression, targeted treatments, and colon cancer pathogenesis. Such approaches are routinely being applied and result in large datasets whose analysis is increasingly becoming dependent on machine learning (ML) algorithms that have been demonstrated to be computationally efficient platforms for the identification of variables across such high-dimensional datasets. Methods: We developed a novel ML-based experimental design to study CRC gene associations. Six different machine learning methods were employed as classifiers to identify genes that can be used as diagnostics for CRC using gene expression and clinical datasets. The accuracy, sensitivity, specificity, F1 score, and area under receiver operating characteristic (AUROC) curve were derived to explore the differentially expressed genes (DEGs) for CRC diagnosis. Gene ontology enrichment analyses of these DEGs were performed and predicted gene signatures were linked with miRNAs. Results: We evaluated six machine learning classification methods (Adaboost, ExtraTrees, logistic regression, naïve Bayes classifier, random forest, and XGBoost) across different combinations of training and test datasets over GEO datasets. The accuracy and the AUROC of each combination of training and test data with different algorithms were used as comparison metrics. Random forest (RF) models consistently performed better than other models. In total, 34 genes were identified and used for pathway and gene set enrichment analysis. Further mapping of the 34 genes with miRNA identified interesting miRNA hubs genes. Conclusions: We identified 34 genes with high accuracy that can be used as a diagnostics panel for CRC.

Alterations in the Gut Microbiota and Their Metabolites in Colorectal Cancer: Recent Progress and Future Prospects

Colorectal cancer (CRC) is a leading cause of cancer morbidity and mortality worldwide. The etiology and pathogenesis of CRC remain unclear. A growing body of evidence suggests dysbiosis of gut bacteria can contribute to the occurrence and development of CRC by generating harmful metabolites and changing host physiological processes. Metabolomics, a systems biology method, will systematically study the changes in metabolites in the physiological processes of the body, eventually playing a significant role in the detection of metabolic biomarkers and improving disease diagnosis and treatment. Metabolomics, in particular, has been highly beneficial in tracking microbially derived metabolites, which has substantially advanced our comprehension of host-microbiota metabolic interactions in CRC. This paper has briefly compiled recent research progress of the alterations of intestinal flora and its metabolites associated with CRC and the application of association analysis of metabolomics and gut microbiome in the diagnosis, prevention, and treatment of CRC; furthermore, we discuss the prospects for the problems and development direction of this association analysis in the study of CRC. Gut microbiota and their metabolites influence the progression and causation of CRC, and the association analysis of metabolomics and gut microbiome will provide novel strategies for the prevention, diagnosis, and therapy of CRC.

Multiple amplified microRNAs monitoring in living cells based on fluorescence quenching of Mo2B and hybridization chain reaction

Imaging intracellular microRNAs (miRNAs) demonstrated an essential role in exposing their biological and pathological functions. However, the detection of sequence-specific miRNAs in living cells remains a key challenge. Herein, a facile amplified multiple intracellular miRNAs imaging platform was constructed based on Mo₂B nanosheets (NSs) fluorescence (FL) quenching and hybridization chain reaction (HCR). The Mo₂B NSs demonstrated strong interaction with the hairpin probes (HPs), ssDNA loop, and excellent multiple FL dyes quenching performance, achieving ultralow background signal. After transfection, the HPs recognized specific targets miRNAs, the corresponding HCR was triggered to produce tremendous DNA-miRNA duplex helixes, which dissociated from the surface of the Mo₂B NSs to produce strong FL for miRNAs detection. It realized to image multiple miRNAs biomarkers in different cells to discriminate cancer cells from normal cells owing to the excellent sensitivity, and the regulated expression change of miRNAs in cancer cells was also successfully monitored. The facile and versatile Mo₂B-based FL quenching platform open an avenue to profile miRNAs expression pattern in living cells, and has great applications in miRNAs based biological and biomedical research.

The Application of Metabolomics in Recent Colorectal Cancer Studies: A State-of-the-Art Review

Simple Summary

Colorectal Cancer (CRC) is one of the leading causes of cancer-related death in the United States. Current diagnosis techniques are either highly invasive or lack sensitivity, suggesting the need for alternative techniques for biomarker detection. Metabolomics represents one such technique with great promise in identifying CRC biomarkers with high sensitivity and specificity, but thus far is rarely employed in a clinical setting. In order to provide a framework for future clinical usage, we characterized dysregulated metabolites across recent literature, identifying metabolites dysregulated across a variety of biospecimens. We additionally put special focus on the interplay of the gut microbiome and perturbed metabolites in CRC. We were able to identify many metabolites showing consistent dysregulation in CRC, demonstrating the value of metabolomics as a promising diagnostic technique.

Abstract

Colorectal cancer (CRC) is a highly prevalent disease with poor prognostic outcomes if not diagnosed in early stages. Current diagnosis techniques are either highly invasive or lack sufficient sensitivity. Thus, identifying diagnostic biomarkers of CRC with high sensitivity and specificity is desirable. Metabolomics represents an analytical profiling technique with great promise in identifying such biomarkers and typically represents a close tie with the phenotype of a specific disease. We thus conducted a systematic review of studies reported from January 2012 to July 2021 relating to the detection of CRC biomarkers through metabolomics to provide a collection of knowledge for future diagnostic development. We identified thirty-seven metabolomics studies characterizing CRC, many of which provided metabolites/metabolic profile-based diagnostic models with high sensitivity and specificity. These studies demonstrated that a great number of metabolites can be differentially regulated in CRC patients compared to healthy controls, adenomatous polyps, or across stages of CRC. Among these metabolite biomarkers, especially dysregulated were certain amino acids, fatty acids, and lysophosphatidylcholines. Additionally, we discussed the contribution of the gut bacterial population to pathogenesis of CRC through their modulation to fecal metabolite pools and summarized the established links in the literature between certain microbial genera and altered metabolite levels in CRC patients. Taken together, we conclude that metabolomics presents itself as a promising and effective method of CRC biomarker detection.

Circadian Clock Genes Are Correlated with Prognosis and Immune Cell Infiltration in Colon Adenocarcinoma

Background

Colon adenocarcinoma (COAD) is a malignancy with a high incidence and is associated with poor quality of life. Dysfunction of circadian clock genes and disruption of normal rhythms are associated with the occurrence and progression of many cancer types. However, studies that systematically describe the prognostic value and immune-related functions of circadian clock genes in COAD are lacking.

Methods

Genomic data obtained from The Cancer Genome Atlas (TCGA) database was analyzed for expression level, mutation status, potential biological functions, and prognostic performance of core circadian clock genes in COAD. Their correlations with immune infiltration and TMB/MSI score were analyzed by Spearman's correlation analysis. Pearson's correlation analysis was performed to analyze their associations with drug sensitivity. Lasso Cox regression analysis was performed to construct a prognosis signature. Moreover, an mRNA-miRNA-lncRNA regulatory axis was also detected by ceRNA network.

Results

In COAD tissues, the mRNA levels of CLOCK, CRY1, and NR1D1 were increased, while the mRNA levels of ARNTL, CRY2, PER1, PER3, and RORA were decreased. We also summarized the relative genetic mutation variation landscape. GO and KEGG pathway analyses demonstrated that these circadian clock genes were primarily correlated with the regulation of circadian rhythms and glucocorticoid receptor signaling pathways. COAD patients with high CRY2, NR1D1, and PER2 expression had worse prognosis. A prognostic model constructed based on the 9 core circadian clock genes predicted the COAD patients' overall survival with medium to high accuracy. A significant association between prognostic circadian clock genes and immune cell infiltration was found. Moreover, the lncRNA KCNQ1OT1/hsa-miRNA-32-5p/PER2/CRY2 regulatory axis in COAD was also detected through a mRNA-miRNA-lncRNA network.

Conclusion

Our results identified CRY2, NR1D1, and PER2 as potential prognostic biomarkers for COAD patients and correlated their expression with immune cell infiltration. The lncRNA KCNQ1OT1/hsa-miRNA-32-5p/PER2/CRY2 regulatory axis was detected in COAD and might play a vital role in the occurrence and progression of COAD.

Interplay between Genome, Metabolome and Microbiome in Colorectal Cancer

Simple Summary

The development of colorectal cancer (CRC) is influenced by the environment, genetics and microbiota. Microbiome and metabolome analyses allowed for the finding of factors and markers associated with adenoma and CRC risk, but the interaction of host genomics with those omic layers remains unclear. Thus, our aim is to add host genome information to find new factors and markers associated with adenoma and CRC risk or to propose biological mechanisms involved in the risk. We found interactions between different omic layers that could be biologically relevant, and the three layers gave complementary information to predict adenoma and CRC risk. Our findings will help to find new markers for adenoma and CRC risk and to analyze biological mechanisms involved in adenoma and CRC development.

Abstract

Background: Colorectal cancer (CRC), a major health concern, is developed depending on environmental, genetic and microbial factors. The microbiome and metabolome have been analyzed to study their role in CRC. However, the interplay of host genetics with those layers in CRC remains unclear. Methods: 120 individuals were sequenced and association analyses were carried out for adenoma and CRC risk, and for selected components of the microbiome and metabolome. The epistasis between genes located in cholesterol pathways was analyzed; modifiable risk factors were studied using Mendelian randomization; and the three omic layers were used to integrate their data and to build risk prediction models. Results: We detected genetic variants that were associated to components of metabolome or microbiome and adenoma or CRC risk (e.g., in LINC01605, PROKR2 and CCSER1 genes). In addition, we found interactions between genes of cholesterol metabolism, and HDL cholesterol levels affected adenoma (p = 0.0448) and CRC (p = 0.0148) risk. The combination of the three omic layers to build risk prediction models reached high AUC values (>0.91). Conclusions: The use of the three omic layers allowed for the finding of biological mechanisms related to the development of adenoma and CRC, and each layer provided complementary information to build risk prediction models.

Multi-Omics Advancements towards Plasmodium vivax Malaria Diagnosis

Plasmodium vivax malaria is one of the most lethal infectious diseases, with 7 million infections annually. One of the roadblocks to global malaria elimination is the lack of highly sensitive, specific, and accurate diagnostic tools. The absence of diagnostic tools in particular has led to poor differentiation among parasite species, poor prognosis, and delayed treatment. The improvement necessary in diagnostic tools can be broadly grouped into two categories: technologies-driven and omics-driven progress over time. This article discusses the recent advancement in omics-based malaria for identifying the next generation biomarkers for a highly sensitive and specific assay with a rapid and antecedent prognosis of the disease. We summarize the state-of-the-art diagnostic technologies, the key challenges, opportunities, and emerging prospects of multi-omics-based sensors.

Gut microbiota-derived metabolites in CRC progression and causation

BACKGROUND

Based on recent research reports, dysbiosis and improper concentrations of microbial metabolites in the gut may result into the carcinogenesis of colorectal cancer. Recent advancement also highlights the involvement of bacteria and their secreted metabolites in the cancer causation. Gut microbial metabolites are functional output of the host-microbiota interactions and produced by anaerobic fermentation of food components in the diet. They contribute to influence variety of biological mechanisms including inflammation, cell signaling, cell-cycle disruption which are majorly disrupted in carcinogenic activities.

PURPOSE

In this review, we intend to discuss recent updates and possible molecular mechanisms to provide the role of bacterial metabolites, gut bacteria and diet in the colorectal carcinogenesis. Recent evidences have proposed the role of bacteria, such as Fusobacterium nucleaturm, Streptococcus bovis, Helicobacter pylori, Bacteroides fragilis and Clostridium septicum, in the carcinogenesis of CRC. Metagenomic study confirmed that these bacteria are in increased abundance in CRC patient as compared to healthy individuals and can cause inflammation and DNA damage which can lead to development of cancer. These bacteria produce metabolites, such as secondary bile salts from primary bile salts, hydrogen sulfide, trimethylamine-N-oxide (TMAO), which are likely to promote inflammation and subsequently cancer development.

CONCLUSION

Recent studies suggest that gut microbiota-derived metabolites have a role in CRC progression and causation and hence, could be implicated in CRC diagnosis, prognosis and therapy.

Exploring the Inflammatory Pathogenesis of Colorectal Cancer

Colorectal cancer is one of the most commonly diagnosed cancers worldwide. Traditionally, mechanisms of colorectal cancer formation have focused on genetic alterations including chromosomal damage and microsatellite instability. In recent years, there has been a growing body of evidence supporting the role of inflammation in colorectal cancer formation. Multiple cytokines, immune cells such T cells and macrophages, and other immune mediators have been identified in pathways leading to the initiation, growth, and metastasis of colorectal cancer. Outside the previously explored mechanisms and pathways leading to colorectal cancer, initiatives have been shifted to further study the role of inflammation in pathogenesis. Inflammatory pathways have also been linked to some traditional risk factors of colorectal cancer such as obesity, smoking and diabetes, as well as more novel associations such as the gut microbiome, the gut mycobiome and exosomes. In this review, we will explore the roles of obesity and diet, smoking, diabetes, the microbiome, the mycobiome and exosomes in colorectal cancer, with a specific focus on the underlying inflammatory and metabolic pathways involved. We will also investigate how the study of colon cancer from an inflammatory background not only creates a more holistic and inclusive understanding of this disease, but also creates unique opportunities for prevention, early diagnosis and therapy.

Mesenchymal Stem Cell-derived Extracellular Vesicles Transmitting MicroRNA-34a-5p Suppress Tumorigenesis of Colorectal Cancer Through c-MYC/DNMT3a/PTEN Axis

Mesenchymal stem cell–derived extracellular vesicles (MSC-EV) can transport microRNAs (miRNAs) into colorectal cancer (CRC) cells, thus to inhibit the malignant phenotype of cancer cells. Whether MSC-EV could deliver miR-34a-5p to suppress CRC development was surveyed through the research. miR-34a-5p, c-MYC, DNA methyltransferase 3a (DNMT3a), and phosphatase and tensin homolog deleted on chromosome 10 (PTEN) expression were measured in CRC tissues and cell lines. miR-34a-5p and c-MYC expression were altered by transfection in HCT-116 cells. MSC-EV were transfected with miR-34a-5p- and c-MYC-related oligonucleotides and co-cultured with HCT-116 cells. HCT-116 cell growth after treatment was observed. Furthermore, the functional roles of miR-34a-5p and c-MYC were explored in vivo. The combined interactions of miR-34a-5p/c-MYC/DNMT3a/PTEN axis were assessed. miR-34a-5p and PTEN were downregulated while c-MYC and DNMT3a were upregulated in CRC. Depletion of miR-34a-5p drove while that of c-MYC restricted CRC cell growth. MSC-EV retarded CRC progression. Moreover, MSC-EV carrying overexpressed miR-34a-5p or depleted c-MYC further disrupted CRC cell progression. miR-34a-5p targeted c-MYC to regulate DNMT3a and PTEN. c-MYC overexpression abrogated EV-derived miR-34a-5p upregulation-induced effects on CRC. Restoring miR-34a-5p or depleting c-MYC in MSC-EV limited CRC tumor formation. MSC-EV-derived miR-34a-5p depresses CRC development through modulating the binding of c-MYC to DNMT3a and epigenetically regulating PTEN.

Systems-level biomarkers identification and drug repositioning in colorectal cancer

Colorectal cancer (CRC) is the most commonly diagnosed fatal cancer in both women and men worldwide. CRC ranked second in mortality and third in incidence in 2020. It is difficult to diagnose CRC at an early stage as there are no clinical symptoms. Despite advances in molecular biology, only a limited number of biomarkers have been translated into routine clinical practice to predict risk, prognosis and response to treatment. In the last decades, systems biology approaches at the omics level have gained importance. Over the years, several biomarkers for CRC have been discovered in terms of disease diagnosis and prognosis. On the other hand, a few drugs are being developed and used in clinics for the treatment of CRC. However, the development of new drugs is very costly and time-consuming as the research and development takes about 10 years and more than $1 billion. Therefore, drug repositioning (DR) could save time and money by establishing new indications for existing drugs. In this review, we aim to provide an overview of biomarkers for the diagnosis and prognosis of CRC from the systems biology perspective and insights into DR approaches for the prevention or treatment of CRC.

Prognostic and immunological roles of Fc fragment of IgG binding protein in colorectal cancer

Valuable diagnostic and prognostic biomarkers are urgently needed for colorectal cancer (CRC), which is one of the leading causes of mortality worldwide. Previous studies have reported altered expression of a mucin-like protein Fc fragment of IgG binding protein (FCGBP) in various types of cancer, but its potential diagnostic, prognostic and immunological roles in CRC remain to be determined. Therefore, the aim of current study was to investigate the potential roles of FCGBP in CRC. The present study investigated FCGBP mutations and changes in its expression levels using a combination of microarray and public dataset analyses, as well as immunohistochemistry. The results demonstrated a 10.5% mutation frequency in the FCGBP coding sequence in CRC tissues, and identified decreased FCGBP mRNA or protein expression levels in colorectal adenoma and CRC (compared with those in normal colorectal tissues from healthy control subjects), including pathologically advanced CRC (stage III+IV vs. I+II). Survival analysis using the GEPIA and Kaplan-Meier Plotter databases revealed that low FCGBP expression levels were associated with short overall, disease-free, relapse-free and event-free survival times in patients with CRC. Notably, analysis using the online Tumor IMmune Estimation Resource database revealed a positive correlation between FCGBP expression levels and the extent of infiltrating immune cells, such as B cells and dendritic cells. Consistently, the expression levels of most markers (51/57) for various types of immune cells were significantly correlated with FCGBP expression levels in CRC tissues. These findings suggested that FCGBP may serve as a diagnostic and prognostic biomarker, and that FCGBP may be associated with immune infiltration in CRC.

Challenges of diagnostic genomics in Latin America

Cancer genome sequencing methods have now become essential for diagnostic purposes, for devising treatment strategies, and for monitoring disease regression and progression. However, access to these benefits has not permeated homogeneously throughout the world; certain regions, such as Latin America, have been slower at adopting these technologies in terms of their routine use, development and patient access. There are also differences among Latin American subregions with respect to their prioritized types of neoplasia and the drugs that are available and approved in them. An overview of the current situation, including the status of genomics for cancer diagnostics and efforts by type of cancer is presented. In addition, we discuss the perspective of initiatives, alliances, and educational/research programs that pledge to make cancer genomics diagnosis a reality for Latin American individuals' health.

What Is Known about Theragnostic Strategies in Colorectal Cancer

Despite the paradigmatic shift occurred in recent years for defined molecular subtypes in the metastatic setting treatment, colorectal cancer (CRC) still remains an incurable disease in most of the cases. Therefore, there is an urgent need for new tools and biomarkers for both early tumor diagnosis and to improve personalized treatment. Thus, liquid biopsy has emerged as a minimally invasive tool that is capable of detecting genomic alterations from primary or metastatic tumors, allowing the prognostic stratification of patients, the detection of the minimal residual disease after surgical or systemic treatments, the monitoring of therapeutic response, and the development of resistance, establishing an opportunity for early intervention before imaging detection or worsening of clinical symptoms. On the other hand, preclinical and clinical evidence demonstrated the role of gut microbiota dysbiosis in promoting inflammatory responses and cancer initiation. Altered gut microbiota is associated with resistance to chemo drugs and immune checkpoint inhibitors, whereas the use of microbe-targeted therapies including antibiotics, pre-probiotics, and fecal microbiota transplantation can restore response to anticancer drugs, promote immune response, and therefore support current treatment strategies in CRC. In this review, we aim to summarize preclinical and clinical evidence for the utilization of liquid biopsy and gut microbiota in CRC.