From genotype to functional phenotype: unraveling the metabolomic features of colorectal cancer

Quantum-like environment adaptive model for creation of phenotype

The textbook conceptualization of phenotype creation, "genotype (G) + environment (E) + genotype & environment interactions (GE) ↦ phenotype (Ph)", is modeled with open quantum systems theory (OQST) or more generally with adaptive dynamics theory (ADT). The model is quantum-like, i.e., it is not about quantum physical processes in biosystems. Generally such modeling is about applications of the quantum formalism and methodology outside of physics. Macroscopic biosystems, in our case genotypes and phenotypes, are treated as information processors which functioning matches the laws of quantum information theory. Phenotypes are the outputs of the E-adaptation processes described by the quantum master equation, Gorini-Kossakowski-Sudarshan-Lindblad equation (GKSL). Its stationary states correspond to phenotypes. We highlight the class of GKSL dynamics characterized by the camel-like graphs of (von Neumann) entropy: in the process of E-adaptation phenotype's state entropy (disorder) first increases and then falls down - a stable and well-ordered phenotype is created. Traits, an organism's phenotypic characteristics, are modeled within the quantum measurement theory, as generally unsharp observables given by positive operator valued measures (POVMs. This paper is also a review on the methods and mathematical apparatus of quantum information biology.

Insight into Fructose-to-Sucrose Ratio as the Potential Target of Urinalysis in Bladder Cancer

Bladder cancer usually has been diagnosed in elderly patients as it stays asymptomatic until it presents. Current detection methods for bladder cancer cannot be considered as an adequate screening strategy due to their high invasiveness and low sensitivity. However, there remains uncertainty about targets with high sensitivity and specificity for non-invasive bladder cancer examination. Our study aims to investigate the actionable non-invasive screening biomarkers in bladder cancer. Here, we employed scRNA-seq to explore the crucial biological processes for bladder cancer development. We then utilized bidirectional Mendelian randomization (MR) analysis to explore the bidirectional causal relationship between ATP-associated metabolites in urine and bladder cancer. Lastly, we used a BBN-induced mouse model of bladder cancer to validate the crucial gene identified by scRNA-seq and MR analysis. We found that (1) the ATP metabolism process plays a critical role in bladder cancer development; (2) there is a bidirectional and negative causal relationship between fructose-to-sucrose ratio in urine and the risk of bladder cancer; and (3) the higher expression of TPI1, a critical gene in the fructose metabolism pathway, was validated in BBN-induced bladder tumors. Our results reveal that fructose-to-sucrose ratio can serve as a potential target of urinalysis in bladder cancer.

Cold Versus Hot Snare Polypectomy for Colorectal Polyps: An Updated Systematic Review and Meta-analysis of Randomized Controlled Trials

BACKGROUND AND OBJECTIVE

Endoscopic polypectomy is an excellent tool for colon cancer prevention. With the innovation of novel resection techniques, the best method is still being investigated. Hence, we aim to evaluate the efficacy and safety of cold snare polypectomy (CSP) versus hot snare polypectomy (HSP) for colorectal polyp resection.

METHODS

A systematic review and meta-analysis synthesizing evidence from randomized controlled trials retrieved from PubMed, EMBASE, WOS, SCOPUS, and CENTRAL until July 16, 2022. We pooled dichotomous outcomes using risk ratio (RR) with the corresponding CI. This review's protocol was prospectively registered in PROSPERO with ID: CRD42022347496.

RESULTS

We included 18 randomized controlled trials with a total of 4317 patients and 7509 polyps. Pooled RR favored HSP regarding the complete resection rate (RR: 0.96 with 95% CI: 0.95, 1, P = 0.03) and local recurrence incidence (RR: 5.74 with 95% CI: 1.27, 25.8, P = 0.02). Pooled RR favored CSP regarding the colonoscopy time (mean difference: -6.50 with 95% CI: -7.55, -5.44, P = 0.00001) and polypectomy time (mean difference: -57.36 with 95% CI: -81.74, -32.98, P = 0.00001). There was no difference regarding the incidence of immediate bleeding ( P = 0.06) and perforation ( P = 0.39); however, HSP was associated with more incidence of delayed bleeding ( P = 0.01), abdominal pain ( P = 0.007), and postresection syndrome ( P = 0.02).

DISCUSSION

HSP is associated with a higher complete resection and lower recurrence rates; however, HSP is also associated with a higher incidence of adverse events. Therefore, improving the complete resection rate with CSP still warrants more innovation, giving the technique safety and shorter procedure duration.

CircRNAs: emerging factors for regulating glucose metabolism in colorectal cancer

Colorectal cancer is a malignant disease with a high incidence and low survival rate, and the effectiveness of traditional treatments, such as surgery and radiotherapy, is very limited. CircRNAs, a kind of stable endogenous circular RNA, generally function by sponging miRNAs and binding or translating proteins. CircRNAs have been identified to play an important role in regulating the proliferation and metabolism of CRC. In recent years, many reports have indicated that by regulating the expression of glycolysis-related proteins, such as GLUT1 and HK2, or directly translating proteins, circRNAs can promote the Warburg effect in cancer cells, thereby driving CRC metabolism. Moreover, the Warburg effect increases lactate production in cancer cells and promotes acidification of the TME, which further drives cancer progression. In this review, we summarized the remarkable role of circRNAs in regulating glucose metabolism in CRC in recent years, which might be useful for finding new targets for the clinical treatment of CRC.

Predicting ecosystem metaphenome from community metagenome: A grand challenge for environmental biology

Elucidating how an organism's characteristics emerge from its DNA sequence has been one of the great triumphs of biology. This triumph has cumulated in sophisticated computational models that successfully predict how an organism's detailed phenotype emerges from its specific genotype. Inspired by that effort's vision and empowered by its methodologies, a grand challenge is described here that aims to predict the biotic characteristics of an ecosystem, its metaphenome, from nucleic acid sequences of all the species in its community, its metagenome. Meeting this challenge would integrate rapidly advancing abilities of environmental nucleic acids (eDNA and eRNA) to identify organisms, their ecological interactions, and their evolutionary relationships with advances in mechanistic models of complex ecosystems. Addressing the challenge would help integrate ecology and evolutionary biology into a more unified and successfully predictive science that can better help describe and manage ecosystems and the services they provide to humanity.

A Comprehensive Metabolomics Analysis of Fecal Samples from Advanced Adenoma and Colorectal Cancer Patients

Accurate diagnosis of colorectal cancer (CRC) still relies on invasive colonoscopy. Noninvasive methods are less sensitive in detecting the disease, particularly in the early stage. In the current work, a metabolomics analysis of fecal samples was carried out by ultra-high-performance liquid chromatography–tandem mass spectroscopy (UPLC-MS/MS). A total of 1380 metabolites were analyzed in a cohort of 120 fecal samples from patients with normal colonoscopy, advanced adenoma (AA) and CRC. Multivariate analysis revealed that metabolic profiles of CRC and AA patients were similar and could be clearly separated from control individuals. Among the 25 significant metabolites, sphingomyelins (SM), lactosylceramides (LacCer), secondary bile acids, polypeptides, formiminoglutamate, heme and cytidine-containing pyrimidines were found to be dysregulated in CRC patients. Supervised random forest (RF) and logistic regression algorithms were employed to build a CRC accurate predicted model consisting of the combination of hemoglobin (Hgb) and bilirubin E,E, lactosyl-N-palmitoyl-sphingosine, glycocholenate sulfate and STLVT with an accuracy, sensitivity and specificity of 91.67% (95% Confidence Interval (CI) 0.7753–0.9825), 0.7 and 1, respectively.

An 11-gene signature for the prediction of systemic recurrences in colon adenocarcinoma

Background

Prognosis varies among patients within the same colon adenocarcinoma (COAD) stage, indicating the need for reliable molecular markers to enable individualized treatment. This study aimed to investigate gene signatures that can be used for better prognostic prediction of COAD.

Methods

Gene-expression profiles of COAD patients were obtained from the Gene Expression Omnibus database (n = 332) and The Cancer Genome Atlas database (n = 431). The relationship between gene signature and relapse-free survival was analysed in the training set (n = 93) and validated in the internal validation set (n = 94) and external validation sets (n = 145 and 431).

Results

Overall, 11 genes (N-myc downstream regulated gene 1 [NDRG1], fms-like tyrosine kinase 1 [FLT1], lipopolysaccharide binding protein [LBP], fatty acid binding protein 4 [FABP4], adiponectin gene [ADIPOQ], angiotensinogen gene [AGT], activin A receptor, type II-like kinase 1 [ACVRL1], CC chemokine ligand 11 [CCL11], cell division cycle 42 [CDC42], T-cell receptor alpha variable 9_2 [TRAV9_2], and proopiomelanocortin [POMC]) were identified by univariable and least absolute shrinkage and selection operator (LASSO) Cox regression analyses. Based on the risk-score model, the patients were grouped into the high-risk or low-risk groups using the median risk score as the cut-off. The area under the curve (AUC) values for 1-, 3-, and 5-year recurrence were 0.970, 0.849, and 0.859, respectively. Patients in the high-risk group had significantly poorer relapse-free survival than did those in the low-risk group. The predictive accuracy of the 11-gene signature was proven in the validation sets. Our gene signature showed better predictive performance for 1-, 3-, and 5-year recurrence than did the other four models.

Conclusions

The 11-gene signature showed good performance in predicting recurrence in COAD. The accuracy of the signature for prognostic classification requires further confirmation.

Seeding metastases: The role and clinical utility of circulating tumour cells

Peripheral human blood is a readily-accessible source of patient material in which circulating tumour cells (CTCs) can be found. Their isolation and characterization holds the potential to provide prognostic value for various solid cancers. Enumeration of CTCs from blood is becoming a common practice in informing prognosis and may guide therapy decisions. It is further recognized that enumeration alone does not capture perspective on the heterogeneity of tumours and varying functional abilities of the CTCs to interact with the secondary microenvironment. Characterizing the isolated CTCs further, in particular assessing their functional abilities, can track molecular changes in the disease progress. As a step towards identifying a suite of functional features of CTCs that could aid in clinical decisions, developing a CTC isolation technique based on extracellular matrix (ECM) interactions may provide a more solid foundation for isolating the cells of interest. Techniques based on size, charge, density, and single biomarkers are not sufficient as they underutilize other characteristics of cancer cells. The ability of cancer cells to interact with ECM proteins presents an opportunity to utilize their full character in capturing, and also allows assessment of the features that reveal how cells might behave at secondary sites during metastasis. This article will review some common techniques and recent advances in CTC capture technologies. It will further explore the heterogeneity of the CTC population, challenges they experience in their metastatic journey, and the advantages of utilizing an ECM-based platform for CTC capture. Lastly, we will discuss how tailored ECM approaches may present an optimal platform to capture an influential heterogeneous population of CTCs.

A pilot study for colorectal carcinoma screening by instant metabolomic profiles using conductive polymer spray ionization mass spectrometry

BACKGROUND

The rapid and accurate discrimination of colorectal carcinoma (CRC) and polyps at the molecular level enables early intervention of CRC, which can greatly improve the 5-year survival rate of patients. Here we reported the potential of conductive polymer spray ionization mass spectrometry (CPSI-MS) in successfully screening CRC according to the serum metabolic profile.

METHODS

Trace intravenous blood (50 μL) was collected from 60 colorectal carcinoma (CRC) and 60 polyp patients, respectively. After centrifugation, serum (2 μL) was loaded onto the tip of conductive polymer to form a dried serum spot. When the 5 μL methanol-water (1:1, v/v) extraction solvent was spiked onto the dried serum spot followed with +4.5 kV high voltage applied on the polymer tip, the extracted components will be ionized and carried into the MS system for direct metabolic profiling.

FINDINGS

There were 51 metabolites discovered to be significantly changed in CRC serum compared to polyps. Combining these metabolites as the characteristic panel, the ideal diagnostic performance was achieved by Lasso regression model with the accuracy of 88.3%.

INTERPRETATION

This pilot study demonstrated the potential of CPSI-MS as a cost-effective tool in large-scale CRC screening in the high-risk population.

Identification of a Novel Immune-Related CpG Methylation Signature to Predict Prognosis in Stage II/III Colorectal Cancer

With the increasing incidence of colorectal cancer (CRC) and continued difficulty in treating it using immunotherapy, there is an urgent need to identify an effective immune-related biomarker associated with the survival and prognosis of patients with this disease. DNA methylation plays an essential role in maintaining cellular function, and changes in methylation patterns may contribute to the development of autoimmunity, aging, and cancer. In this study, we aimed to identify a novel immune-related methylated signature to aid in predicting the prognosis of patients with CRC. We investigated DNA methylation patterns in patients with stage II/III CRC using datasets from The cancer genome atlas (TCGA). Overall, 182 patients were randomly divided into training (n = 127) and test groups (n = 55). In the training group, five immune-related methylated CG sites (cg11621464, cg13565656, cg18976437, cg20505223, and cg20528583) were identified, and CG site-based risk scores were calculated using univariate Cox proportional hazards regression in patients with stage II/III CRC. Multivariate Cox regression analysis indicated that methylated signature was independent of other clinical parameters. The Kaplan-Meier analysis results showed that CG site-based risk scores could significantly help distinguish between high- and low-risk patients in both the training (P = 0.000296) and test groups (P = 0.022). The area under the receiver operating characteristic curve in the training and test groups were estimated to be 0.771 and 0.724, respectively, for prognosis prediction. Finally, stratified analysis results suggested the remarkable prognostic value of CG site-based risk scores in CRC subtypes. We identified five methylated CG sites that could be used as an efficient overall survival (OS)-related biomarker for stage II/III CRC patients.

Insulin-Like Growth Factor 1 (IGF-1) Signaling in Glucose Metabolism in Colorectal Cancer

Colorectal cancer (CRC) is one of the most common aggressive carcinoma types worldwide, characterized by unfavorable curative effect and poor prognosis. Epidemiological data re-vealed that CRC risk is increased in patients with metabolic syndrome (MetS) and its serum components (e.g., hyperglycemia). High glycemic index diets, which chronically raise post-prandial blood glucose, may at least in part increase colon cancer risk via the insulin/insulin-like growth factor 1 (IGF-1) signaling pathway. However, the underlying mechanisms linking IGF-1 and MetS are still poorly understood. Hyperactivated glucose uptake and aerobic glycolysis (the Warburg effect) are considered as a one of six hallmarks of cancer, including CRC. However, the role of insulin/IGF-1 signaling during the acquisition of the Warburg metabolic phenotypes by CRC cells is still poorly understood. It most likely results from the interaction of multiple processes, directly or indirectly regulated by IGF-1, such as activation of PI3K/Akt/mTORC, and Raf/MAPK signaling pathways, activation of glucose transporters (e.g., GLUT1), activation of key glycolytic enzymes (e.g., LDHA, LDH5, HK II, and PFKFB3), aberrant expression of the oncogenes (e.g., MYC, and KRAS) and/or overexpression of signaling proteins (e.g., HIF-1, TGF-β1, PI3K, ERK, Akt, and mTOR). This review describes the role of IGF-1 in glucose metabolism in physiology and colorectal carcinogenesis, including the role of the insulin/IGF system in the Warburg effect. Furthermore, current therapeutic strategies aimed at repairing impaired glucose metabolism in CRC are indicated.

Comprehensive Transcriptomic Analysis Reveals Prognostic Value of an EMT-Related Gene Signature in Colorectal Cancer

Lymph node metastasis (LNM) is closely related to the postoperative recurrence of colorectal cancer (CRC), and greatly affects patient survival. Conducting Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA), we found that the epithelial-mesenchymal transition (EMT) signaling pathway is the signaling pathway most relevant to the process of LNM. An EMT-related gene signature was identified from a discovery dataset obtained 489 patients using LIMMA and LASSO Cox methods. Six external independent dataset analyses including a total of 1,045 CRC patients and stratification analysis showed that EMT-related gene signature could sort out those high- and low-risk CRC patients accurately. Functional analysis and loss-of-function exploration in vitro and in vivo indicated that the EMT-related-signature-associated coding genes might play functional roles in the sophisticated regulation of CRC proliferation and metastasis. Prognostic nomograms integrating the EMT-related gene signature and clinicopathological risk factors were constructed for use as numerical prediction tools to assess clinical prognosis and clinical decision-makings. The comprehensive transcriptomic analysis in this article highlights the prognostic value of an EMT-related gene signature for postoperative disease recurrence in CRC patients and reveals a potential prognostic and therapeutic biomarker for CRC.

Research Progress of Urine Biomarkers in the Diagnosis, Treatment, and Prognosis of Bladder Cancer

Bladder cancer (BC) is one of the most common tumor with high incidence. Relative to other cancers, BC has a high rate of recurrence, which results in increased mortality. As a result, early diagnosis and life-long monitoring are clinically significant for improving the long-term survival rate of BC patients. At present, the main methods of BC detection are cystoscopy and biopsy; however, these procedures can be invasive and expensive. This can lead to patient refusal and reluctance for monitoring. There are several BC biomarkers that have been approved by the FDA, but their sensitivity, specificity, and diagnostic accuracy are not ideal. More research is needed to identify suitable biomarkers that can be used for early detection, evaluation, and observation. There has been heavy research in the proteomics and genomics of BC and many potential biomarkers have been found. Although the advent of metabonomics came late, with the recent development of advanced analytical technology and bioinformatics, metabonomics has become a widely used diagnostic tool in clinical and biomedical research. It should be emphasized that despite progress in new biomarkers for BC diagnosis, there remains challenges and limitations in metabonomics research that affects its translation into clinical practice. In this chapter, the latest literature on BC biomarkers was reviewed.

Integrated approaches for precision oncology in colorectal cancer: The more you know, the better

Colorectal cancer (CRC) is one of the most common human malignancies accounting for approximately 10 % of worldwide cancer incidence and mortality. While early-stage CRC is mainly a preventable and curable disease, metastatic colorectal cancer (mCRC) remains an unmet clinical need. Moreover, about 25 % of CRC cases are diagnosed only at the metastatic stage. Despite the extensive molecular and functional knowledge on this disease, systemic therapy for mCRC still relies on traditional 5-fluorouracil (5-FU)-based chemotherapy regimens. On the other hand, targeted therapies and immunotherapy have shown effectiveness only in a limited subset of patients. For these reasons, there is a growing need to define the molecular and biological landscape of individual patients to implement novel, rationally driven, tailored therapies. In this review, we explore current and emerging approaches for CRC management such as genomic, transcriptomic and metabolomic analysis, the use of liquid biopsies and the implementation of patients' preclinical avatars. In particular, we discuss the contribution of each of these tools in elucidating patient specific features, with the aim of improving our ability in advancing the diagnosis and treatment of colorectal tumors.

A novel epithelial-mesenchymal transition molecular signature predicts the oncological outcomes in colorectal cancer

Epithelial‐mesenchymal transition (EMT), a biological process involving the transformation of epithelial cells into mesenchymal cells, promotes tumour initiation and metastasis. The aim of this study was to construct an EMT molecular signature for predicting colorectal cancer (CRC) prognosis and evaluate the efficacy of the model. The risk scoring system, constructed by log‐rank test and multivariate Cox regression analysis according to EMT‐related gene expression in CRC patients from TCGA database, demonstrated the highest correlation with prognosis compared with other parameters in CRC patients. The risk scores were significantly correlated with more lymph node metastasis, distal metastasis and advanced clinical stage of CRC. The model was further successfully validated in two independent external cohorts from GEO database. Furthermore, we developed a nomogram to integrate the EMT signature with the pathological stage of CRC, which was found to perform well in predicting the overall survival. Additionally, this risk scoring model was found to be associated with immune cell infiltration, implying a potential role of EMT involved in immunity regulation in tumour microenvironment. Taken together, our novel EMT molecular model may be useful in identifying high‐risk patients who need an intensive follow‐up and more aggressive therapy, finally contributing to more precise individualized therapeutic strategies.

Untargeted Metabolomics Reveals Major Differences in the Plasma Metabolome between Colorectal Cancer and Colorectal Adenomas

Sporadic colorectal cancer is characterized by a multistep progression from normal epithelium to precancerous low-risk and high-risk adenomas to invasive cancer. Yet, the underlying molecular mechanisms of colorectal carcinogenesis are not completely understood. Within the "Metabolomic profiles throughout the continuum of colorectal cancer" (MetaboCCC) consortium we analyzed data generated by untargeted, mass spectrometry-based metabolomics using plasma from 88 colorectal cancer patients, 200 patients with high-risk adenomas and 200 patients with low-risk adenomas recruited within the "Colorectal Cancer Study of Austria" (CORSA). Univariate logistic regression models comparing colorectal cancer to adenomas resulted in 442 statistically significant molecular features. Metabolites discriminating colorectal cancer patients from those with adenomas in our dataset included acylcarnitines, caffeine, amino acids, glycerophospholipids, fatty acids, bilirubin, bile acids and bacterial metabolites of tryptophan. The data obtained discovers metabolite profiles reflecting metabolic differences between colorectal cancer and colorectal adenomas and delineates a potentially underlying biological interpretation.

Prediction of Prognostic Biomarkers and Construction of an Autophagy Prognostic Model for Colorectal Cancer Using Bioinformatics

Objective:

The incidence of colorectal cancer is increasing every year, and autophagy may be related closely to the pathogenesis of colorectal cancer. Autophagy is a natural catabolic mechanism that allows the degradation of cellular components in eukaryotic cells. However, autophagy plays a dual role in tumorigenesis. It not only promotes normal cell survival and tumor growth but also induces cell death and suppresses tumors survival. In addition, the pathogenesis of various conditions, including inflammation, neurodegenerative diseases, or tumors, is associated with abnormal autophagy. The present work aimed to examine the significance of autophagy-related genes (ARGs) in prognosis prediction, to construct an autophagy prognostic model, and to identify independent prognostic factors for colorectal cancer (CRC).

Methods:

This study discovered a total of 36 ARGs in CRC cases using The Cancer Genome Atlas (TCGA) and Human Autophagy-dedicated (HADd) databases along with functional enrichment analysis. Then, an autophagy prognostic model was constructed using univariate Cox regression analysis, and the key prognostic genes were screened. Finally, independent prognostic markers were determined through independent prognostic analysis and clinical correlation analysis of key genes.

Results:

Of the 36 differentially expressed ARGs, 13 were related to prognosis, as determined by univariate Cox regression analysis. A total of 6 key genes were obtained by a multivariate Cox regression analysis. Independent prognostic values were shown by 3 genes, namely, microtubule-associated protein 1 light chain 3 (MAP1LC3C), small GTPase superfamily and Rab family (RAB7A), and WD-repeat domain phosphoinositide-interacting protein 2 (WIPI2) by independent prognostic analysis and clinical correlation.

Conclusions:

In this study, molecular bioinformatics technology was employed to determine and construct a prognostic model of autophagy for colon cancer patients, which revealed 3 autophagy-related features, namely, MAP1LC3C, WIPI2, and RAB7A.

Urinary 1H-NMR Metabolic Signature in Subjects Undergoing Colonoscopy for Colon Cancer Diagnosis

Metabolomics represents a promising non-invasive approach that can be applied to identify biochemical changes in colorectal cancer patients (CRC) and is potentially useful for diagnosis and follow-up. Despite the literature regarding metabolomics CRC-specific profiles, discrimination between metabolic changes specifically related to CRC and intra-individual variability is still a problem to be solved. This was a preliminary case-control study, in which 1H-NMR spectroscopy combined with multivariate statistical analysis was used to profile urine metabolites in subjects undergoing colonoscopy for colon cancer diagnosis. To reduce intra-individual variability, metabolic profiles were evaluated in participants’ urine samples, collected just before the colonoscopy and after a short-term dietary regimen required for the endoscopy procedure. Data obtained highlighted different urinary metabolic profiles between CRC and unaffected subjects (C). The metabolites altered in the CRC urine (acetoacetate, creatine, creatinine, histamine, phenylacetylglycine, and tryptophan) significantly correlated with colon cancer and discriminated with accuracy CRC patients from C patients (receiver operator characteristic (ROC) curve with an area under the curve (AUC) of 0.875; 95% CI: 0.667–1). These results confirm that urinary metabolomic analysis can be a valid tool to improve CRC diagnosis, prognosis, and response to therapy, representing a noninvasive approach that could precede more invasive tests.

Prognostic and predictive value of an autophagy-related signature for early relapse in stages I-III colon cancer

We postulated that expression differences of autophagy-related genes are instrumental in stratifying the risk of early relapse after surgery and evaluating the prognosis of patients with stages I-III colon cancer. Therefore, propensity score matching analysis was performed between patients in early relapse group and long-term survival group from GSE39582 test series and internal validation series. Using Cox regression model, a nine-autophagy-related signature (CAPN2, ATG16L2, TP63, SIRT1, RPS6KB1, PEX3, ATG5, UVRAG, NAF1) was established to classify patients into those at high risk of early relapse (high-risk group), and those at low risk of early relapse (low-risk group). Relapse-free survival (RFS) was significantly different between the two groups in test [hazard ratio (HR): 2.019, 95% confidence interval (CI): 1.362-2.992, P < 0.001], internal validation (HR: 2.464, 95% CI: 1.196-5.079, P < 0.001) and another two external validation series (GSE14333-HR: 2.250, 95% CI: 1.227-4.126, P = 0.007; GSE33113-HR: 5.552, 95% CI: 2.098-14.693, P < 0.001). Then, based on RFS, we developed a nomogram, integrating the nine-autophagy-related classifier and four clinicopathological risk factors to evaluate prognosis of stages I-III colon cancer patients. Time-dependent receiver operating curve at 2 years showed that the integrated signature (area under curve = 0.758) had better prognostic accuracy than American Joint Committee on Cancer TNM stage (area under curve = 0.620). In conclusion, we identified and built a nine-autophagy-related signature, a credible approach to early relapse prediction in stages I-III colon cancer patients, which can assist physicians in devising more efficient therapeutic strategies.

Development and validation of a novel epigenetic signature for predicting prognosis in colon cancer

Epigenetic factors play a critical role in carcinogenesis by imparting a distinct feature to the chromatin architecture. The present study aimed to develop a novel epigenetic signature for evaluating the relapse-free survival of colon cancer patients. Public microarray datasets were acquired from the Gene Expression Omnibus databases: GSE39582, GSE17538, GSE33113, and GSE37892 set. Patients from GSE39582 set were randomized 1:1 into training and internal validation series. Patients were divided into high-risk and low-risk groups in training series based on a set of 11 epigenetic factors (p < .001). The good reproducibility for the prognostic value of the epigenetic signature was confirmed in the internal validation series (p < .001), external validation series (a combination of GSE17538 set, GSE33113 set, and GSE37892 set; p = .018), and entire series (p < .001). Furthermore, a nomogram, which integrated the epigenetic signature, pathological stage, and postoperative chemotherapy, was developed based on the GSE39582 set. The time-dependent receiver operating characteristic curve at 1 year demonstrated that the comprehensive signature presented superior prognostic value than the pathological stage. In conclusion, an epigenetic signature, which could be utilized to divide colon cancer patients into two groups with significantly different risk of relapse, was established. This biomarker would aid in identifying patients who require an intensive follow-up and aggressive therapeutic intervention.

Cancer metabolomic markers in urine: evidence, techniques and recommendations

Urinary tests have been used as noninvasive, cost-effective tools for screening, diagnosis and monitoring of diseases since ancient times. As we progress through the 21st century, modern analytical platforms have enabled effective measurement of metabolites, with promising results for both a deeper understanding of cancer pathophysiology and, ultimately, clinical translation. The first study to measure metabolomic urinary cancer biomarkers using NMR and mass spectrometry (MS) was published in 2006 and, since then, these techniques have been used to detect cancers of the urological system (kidney, prostate and bladder) and nonurological tumours including those of the breast, ovary, lung, liver, gastrointestinal tract, pancreas, bone and blood. This growing field warrants an assessment of the current status of research developments and recommendations to help systematize future research.

Development and Validation of an Autophagy Score Signature for the Prediction of Post-operative Survival in Colorectal Cancer

Background: Survival rates for Colorectal cancer (CRC) patients who experienced early relapse have usually been relatively low. Our study aims at developing an autophagy signature that could help to detect early relapse cases in CRC.

Methods: Propensity score matching analysis was carried out between patients from the early relapse group and the long-term survival group from GSE39582. For both groups, respectively, global autophagy expression changes were then analyzed to identify the differentially expressed prognostic autophagy related genes by conducting Linear Models for Microarray data method analysis. Then, the multi-gene signature was validated in TCGA and Fudan University Shanghai Cancer Center (FUSCC) cohorts. Time-dependent ROC were used to test the efficiency of this signature feature in predicting the prognosis of CRC patients.

Results: 5 autophagy genes were finally identified to build an early relapse classifier. With specific risk score formula, patients were classified into low- or high-risk group. Time-dependent ROC analyses proved its prognostic accuracy, with AUC 0.841 and 0.803 at 1 and 3 years, respectively. Then, we validated its prognostic value in two external validation series (GSE17538 and GSE33113) and proved that the result is indeed significant irrespective of datasets in two external independent validation cohorts (TCGA and FUSCC cohorts). A nomogram was constructed to guide individualized treatment of patients with CRC.

Conclusions: The identification of robust autophagy-related features can effectively classify CRC patients into groups with low and high risk of early relapse. This signature may be used to help select high-risk CRC patients who require more aggressive treatment interventions.

Current and Future Horizons of Patient-Derived Xenograft Models in Colorectal Cancer Translational Research

Our poor understanding of the intricate biology of cancer and the limited availability of preclinical models that faithfully recapitulate the complexity of tumors are primary contributors to the high failure rate of novel therapeutics in oncology clinical studies. To address this need, patient-derived xenograft (PDX) platforms have been widely deployed and have reached a point of development where we can critically review their utility to model and interrogate relevant clinical scenarios, including tumor heterogeneity and clonal evolution, contributions of the tumor microenvironment, identification of novel drugs and biomarkers, and mechanisms of drug resistance. Colorectal cancer (CRC) constitutes a unique case to illustrate clinical perspectives revealed by PDX studies, as they overcome limitations intrinsic to conventional ex vivo models. Furthermore, the success of molecularly annotated "Avatar" models for co-clinical trials in other diseases suggests that this approach may provide an additional opportunity to improve clinical decisions, including opportunities for precision targeted therapeutics, for patients with CRC in real time. Although critical weaknesses have been identified with regard to the ability of PDX models to predict clinical outcomes, for now, they are certainly the model of choice for preclinical studies in CRC. Ongoing multi-institutional efforts to develop and share large-scale, well-annotated PDX resources aim to maximize their translational potential. This review comprehensively surveys the current status of PDX models in translational CRC research and discusses the opportunities and considerations for future PDX development.

1H-NMR Metabolomics Identifies Significant Changes in Metabolism over Time in a Porcine Model of Severe Burn and Smoke Inhalation

Burn injury initiates a hypermetabolic response leading to muscle catabolism and organ dysfunction but has not been well-characterized by high-throughput metabolomics. We examined changes in metabolism over the first 72 h post-burn using proton nuclear magnetic resonance (1H-NMR) spectroscopy and serum from a porcine model of severe burn injury. We sought to quantify the changes in metabolism that occur over time in response to severe burn and smoke inhalation in this preliminary study. Fifteen pigs received 40% total body surface area (TBSA) burns with additional pine bark smoke inhalation. Arterial blood was drawn at baseline (pre-burn) and every 24 h until 72 h post-injury or death. The aqueous portion of each serum sample was analyzed using 1H-NMR spectroscopy and metabolite concentrations were used for principal component analysis (PCA). Thirty-eight metabolites were quantified in 39 samples. Of these, 31 showed significant concentration changes over time (p < 0.05). PCA revealed clustering of samples by time point on a 2D scores plot. The first 48 h post-burn were characterized by high concentrations of histamine, alanine, phenylalanine, and tyrosine. Later timepoints were characterized by rising concentrations of 2-hydroxybutyrate, 3-hydroxybutyrate, acetoacetate, and isovalerate. No significant differences in metabolism related to mortality were observed. Our work highlights the accumulation of organic acids resulting from fatty acid catabolism and oxidative stress. Further studies will be required to relate accumulation of the four organic carboxylates identified in this analysis to outcomes from burn injury.

Serum lipidome screening in patients with stage I non-small cell lung cancer

The ability of early lung cancer diagnosis is an unmet need in clinical practice. Lung cancer metabolomic analyses conducted so far have demonstrated several abnormalities in cancer lipid profile providing a rationale for further study of blood lipidome of the patients. In the present research, we performed a targeted lipidome screening to select molecules that show promise for early lung cancer detection. The study was conducted on serum samples collected from newly diagnosed, stage I non-small cell lung cancer (NSCLC) patients and non-cancer controls. A high-throughput mass spectrometry-based platform with confirmed interlaboratory reproducibility was used. The analyzed profile consisted of acylcarnitines, sphingomyelins, phosphatidylcholines and lysophosphatidylcholines. Among the assayed lipid species, the significant differences between NSCLC and non-cancer subjects were observed in the group of phosphatidylcholines (PC) and lysophosphatidylcholines (lysoPC), especially in the levels of lysoPC a C26:0; lysoPC a C26:1; PC aa C42:4; and PC aa C34:4. The metabolites mentioned above were used to create a multivariate classification model, which reliability was proved by permutation tests as well as external validation. Our study indicated choline-containing phospholipids as potential lung cancer markers. Further investigations of phospholipidome are crucial to better describe the shifts in metabolite composition occurring in lung cancer patients.

A novel gene-pair signature for relapse-free survival prediction in colon cancer

Background

Colon cancer (CC) patients with early relapse usually have a poor prognosis. In this study, we aimed to identify a novel signature to improve the prediction of relapse-free survival (RFS) in CC.

Methods

Four microarray datasets were merged into a training set (n=1,045), and one RNA-sequencing dataset was used as a validation set (n=384). In the training set, microarray meta-analysis screened out 596 common RFS-related genes across datasets, which were used to construct 177,310 gene pairs. Then, the LASSO penalized generalized linear model identified 16 RFS-related gene pairs, and a risk score was calculated for each sample according to the model coefficients.

Results

The risk score demonstrated a good ability in predicting RFS (area under the curve [AUC] at 5 years: 0.724; concordance index [C-index]: 0.642, 95% CI: 0.615–0.669). High-risk patients showed a poorer prognosis than low-risk patients (HR: 3.519, 95% CI: 2.870–4.314). Subgroup analysis reached consistent results when considering multiple confounders. In the validation set, the risk score had a similar performance (AUC at 5 years: 0.697; C-index: 0.696, 95% CI: 0.627–0.766; HR: 2.926, 95% CI: 1.892–4.527). When compared with a 13-gene signature, a 15-gene signature, and TNM stage, the score showed a better performance (P<0.0001; P=0.0004; P=0.0125), especially for the patients with a longer follow-up (R2=0.988, P<0.0001). When the follow-up was >5 years (n=314), the score demonstrated an excellent performance (C-index: 0.869, 95% CI: 0.816–0.922; HR: 13.55, 95% CI: 7.409–24.78).

Conclusion

Our study identified a novel gene-pair signature for prediction of RFS in CC.

A robust gene signature for the prediction of early relapse in stage I-III colon cancer

Colon cancer patients experiencing early relapse consistently exhibited poor survival. The aim of our study was to develop an mRNA signature that can help to detect early relapse cases in stage I-III colon cancer. Public microarray datasets of stage I-III colon cancer samples were extracted from the Gene Expression Omnibus database. Propensity score matching analysis was performed between patients in the early relapse group and the long-term survival group from GSE39582 discovery series (N = 386), and patients were 1 : 1 matched. Global mRNA expression changes were then analyzed between the paired groups to identify the differentially expressed genes. Lasso Cox regression modeling analysis was conducted for the selection of prognostic mRNA. Fifteen mRNA were finally identified to build an early relapse classifier. With specific risk score formula, patients were classified into a high-risk group and a low-risk group. Relapse-free survival was significantly different between the two groups in every series, including discovery [hazard ratio (HR): 2.547, 95% confidence interval (CI): 1.708-3.797, P < 0.001)], internal validation (HR: 5.146, 95% CI: 1.968-13.457, P < 0.001), and external validation (HR: 1.977, 95% CI: 1.295-3.021, P < 0.001) sets of patients. Time-dependent receiver-operating characteristic at 1 year suggested more prognostic accuracy of the classifier [area under curve (AUC = 0.703)] than the American Joint Commission on Cancer tumor-node-metastasis staging system (AUC = 0.659) in all 951 patients. In conclusion, we developed a robust mRNA signature that can effectively classify colon cancer patients into groups with low and high risks of early relapse. This mRNA signature may help select high-risk colon cancer patients who require more aggressive therapeutic intervention.

Towards personalized medicine of colorectal cancer

Efforts in colorectal cancer (CRC) research aim to improve early detection and treatment for metastatic stages which could translate into better prognosis of this disease. One of the major challenges that hinder these efforts is the heterogeneous nature of CRC and involvement of diverse molecular pathways. New large-scale 'omics' technologies are making it possible to generate, analyze and interpret biological data from molecular determinants of CRC. The developments of sophisticated computational analyses would allow information from different omics platforms to be integrated, thus providing new insights into the biology of CRC. Together, these technological advances and an improved mechanistic understanding might allow CRC to be clinically managed at the level of the individual patient. This review provides an account of the current challenges in CRC management and an insight into how new technologies could allow the development of personalized medicine for CRC.

Progress in Metabolomics Standardisation and its Significance in Future Clinical Laboratory Medicine

Today, the technology of 'targeted' based metabolomics is pivotal in the clinical analysis workflow as it provides information of metabolic phenotyping (metabotypes) by enhancing our understanding of metabolism of complex diseases, biomarker discovery for disease development, progression, treatment, and drug function and assessment. This review is focused on surveying and providing a gap analysis on metabolic phenotyping with a focus on targeted based metabolomics from an instrumental, technical point-of-view discussing the state-of-the-art instrumentation, pre- to post- analytical aspects as well as an overall future necessity for biomarker discovery and future (pre-) clinical routine application.

Fecal Metabolome in Hmga1 Transgenic Mice with Polyposis: Evidence for Potential Screen for Early Detection of Precursor Lesions in Colorectal Cancer

Because colorectal cancer (CRC) remains a leading cause of cancer mortality worldwide, more accessible screening tests are urgently needed to identify early stage lesions. We hypothesized that highly sensitive, metabolic profile analysis of stool samples will identify metabolites associated with early stage lesions and could serve as a noninvasive screening test. We therefore applied traveling wave ion mobility mass spectrometry (TWIMMS) coupled with ultraperformance liquid chromatography (UPLC) to investigate metabolic aberrations in stool samples in a transgenic model of premalignant polyposis aberrantly expressing the gene encoding the high mobility group A (Hmga1) chromatin remodeling protein. Here, we report for the first time that the fecal metabolome of Hmga1 mice is distinct from that of control mice and includes metabolites previously identified in human CRC. Significant alterations were observed in fatty acid metabolites and metabolites associated with bile acids (hypoxanthine xanthine, taurine) in Hmga1 mice compared to controls. Surprisingly, a marked increase in the levels of distinctive short, arginine-enriched, tetra-peptide fragments was observed in the transgenic mice. Together these findings suggest that specific metabolites are associated with Hmga1-induced polyposis and abnormal proliferation in intestinal epithelium. Although further studies are needed, these data provide a compelling rationale to develop fecal metabolomic analysis as a noninvasive screening tool to detect early precursor lesions to CRC in humans.

A validated metabolomic signature for colorectal cancer: exploration of the clinical value of metabolomics

Background:

Timely diagnosis and classification of colorectal cancer (CRC) are hindered by unsatisfactory clinical assays. Our aim was to construct a blood-based biomarker series using a single assay, suitable for CRC detection, prognostication and staging.

Methods:

Serum metabolomic profiles of adenoma (N=31), various stages of CRC (N=320) and healthy matched controls (N=254) were analysed by gas chromatography-mass spectrometry (GC-MS). A diagnostic model for CRC was derived by orthogonal partial least squares-discriminant analysis (OPLS-DA) on a training set, and then validated on an independent data set. Metabolomic models suitable for identifying adenoma, poor prognosis stage II CRC and discriminating various stages were generated.

Results:

A diagnostic signature for CRC with remarkable multivariate performance (R²Y=0.46, Q²Y=0.39) was constructed, and then validated (sensitivity 85% specificity 86%). Area under the receiver-operating characteristic curve was 0.91 (95% CI, 0.87–0.96). Adenomas were also detectable (R²Y=0.35, Q²Y=0.26, internal AUROC=0.81, 95% CI, 0.70–0.92). Also of particular interest, we identified models that stratified stage II by prognosis, and classified cases by stage.

Conclusions:

Using a single assay system, a suite of CRC biomarkers based on circulating metabolites enables early detection, prognostication and preliminary staging information. External population-based studies are required to evaluate the repeatability of our findings and to assess the clinical benefits of these biomarkers.

Molecular biomarkers in colorectal carcinoma

Colorectal cancer is a tumor with increasing incidence which represents one of the first leading causes of death worldwide. Gene alterations described for colorectal cancer include genome instability (microsatellite and chromosomal instability), CpG islands methylator phenotype, microRNA, histone modification, protein biomarkers, gene mutations (RAS, BRAF, PI3K, TP53, PTEN) and polymorphisms (APC, CTNNB1, DCC). In this article, biomarkers with prognostic value commonly found in colorectal cancer will be reviewed.

Proceedings of the second international molecular pathological epidemiology (MPE) meeting

Disease classification system increasingly incorporates information on pathogenic mechanisms to predict clinical outcomes and response to therapy and intervention. Technological advancements to interrogate omics (genomics, epigenomics, transcriptomics, proteomics, metabolomics, metagenomics, interactomics, etc.) provide widely open opportunities in population-based research. Molecular pathological epidemiology (MPE) represents integrative science of molecular pathology and epidemiology. This unified paradigm requires multidisciplinary collaboration between pathology, epidemiology, biostatistics, bioinformatics, and computational biology. Integration of these fields enables better understanding of etiologic heterogeneity, disease continuum, causal inference, and the impact of environment, diet, lifestyle, host factors (including genetics and immunity), and their interactions on disease evolution. Hence, the Second International MPE Meeting was held in Boston in December 2014, with aims to: (1) develop conceptual and practical frameworks; (2) cultivate and expand opportunities; (3) address challenges; and (4) initiate the effort of specifying guidelines for MPE. The meeting mainly consisted of presentations of method developments and recent data in various malignant neoplasms and tumors (breast, prostate, ovarian and colorectal cancers, renal cell carcinoma, lymphoma, and leukemia), followed by open discussion sessions on challenges and future plans. In particular, we recognized need for efforts to further develop statistical methodologies. This meeting provided an unprecedented opportunity for interdisciplinary collaboration, consistent with the purposes of the Big Data to Knowledge, Genetic Associations and Mechanisms in Oncology, and Precision Medicine Initiative of the US National Institute of Health. The MPE meeting series can help advance transdisciplinary population science and optimize training and education systems for twenty-first century medicine and public health.