Investigation of correlation between shear wave elastography and lymphangiogenesis in invasive breast cancer and diagnosis of axillary lymph node metastasis.

BACKGROUND: Accurate evaluation of axillary lymph node metastasis (LNM) in breast cancer is very important. A large number of hyperplastic and dilated lymphangiogenesis cases can usually be found in the pericancerous tissue of breast cancer to promote the occurrence of tumor metastasis.Shear wave elastography (SWE) can be used as an important means for evaluating pericancerous stiffness. We determined the stiffness of the pericancerous by SWE to diagnose LNM and lymphangiogenesis in invasive breast cancer (IBC). METHODS: Patients with clinical T1-T2 stage IBC who received surgical treatment in our hospital from June 2020 to December 2020 were retrospectively enrolled. A total of 299 patients were eventually included in the preliminary study, which included an investigation of clinicopathological features, ultrasonic characteristics, and SWE parameters. Multivariable logistic regression analysis was used to establish diagnostic model and evaluated its diagnostic performance of LNM. The correlation among SWE values, collagen volume fraction (CVF), and microlymphatic density (MLD) in primary breast cancer lesions was analyzed in another 97 patients. RESULTS: The logistic regression model is Logit(P)=-1.878 + 0.992*LVI-2.010*posterior feature enhancement + 1.230*posterior feature shadowing + 0.102*posterior feature combined pattern + 0.009*Emax. The optimum cutoff value of the logistic regression model was 0.365, and the AUC (95% CI) was 0.697 (0.636-0.758); the sensitivity (70.7 vs. 54.3), positive predictive value (PPV) (54.0 vs. 50.8), negative predictive value (NPV) (76.9 vs. 69.7), and accuracy (65.2 vs. 61.9) were all higher than Emax. There was no correlation between the SWE parameters and MLD in primary breast cancer lesions. CONCLUSIONS: The logistic regression model can help us to determine LNM, thus providing more imaging basis for the selection of preoperative treatment. The SWE parameter of the primary breast cancer lesion cannot reflect the peritumoral lymphangiogenesis, and we still need to find a new ultrasonic imaging method.

Shaping immune landscape of colorectal cancer by cholesterol metabolites.

Cancer immunotherapies have achieved unprecedented success in clinic, but they remain largely ineffective in some major types of cancer, such as colorectal cancer with microsatellite stability (MSS CRC). It is therefore important to study tumor microenvironment of resistant cancers for developing new intervention strategies. In this study, we identify a metabolic cue that determines the unique immune landscape of MSS CRC. Through secretion of distal cholesterol precursors, which directly activate RORγt, MSS CRC cells can polarize T cells toward Th17 cells that have well-characterized pro-tumor functions in colorectal cancer. Analysis of large human cancer cohorts revealed an asynchronous pattern of the cholesterol biosynthesis in MSS CRC, which is responsible for the abnormal accumulation of distal cholesterol precursors. Inhibiting the cholesterol biosynthesis enzyme Cyp51, by pharmacological or genetic interventions, reduced the levels of intratumoral distal cholesterol precursors and suppressed tumor progression through a Th17-modulation mechanism in preclinical MSS CRC models. Our study therefore reveals a novel mechanism of cancer-immune interaction and an intervention strategy for the difficult-to-treat MSS CRC.

Prognostic value of gut microbiota-derived metabolites in patients with ST-segment elevation myocardial infarction.

BACKGROUND: Studies about the prognostic role of gut microbiota-derived metabolites including phenylacetyl glutamine (PAGln), indoxyl sulfate (IS), lithocholic acid (LCA), deoxycholic acid (DCA), trimethylamine (TMA), trimethylamine N-oxide (TMAO), and its precursor trimethyllysine (TML) are limited in patients with ST-segment elevation myocardial infarction (STEMI). OBJECTIVES: To examine the relationship between plasma metabolite levels and major adverse cardiovascular events (MACEs), including nonfatal MI, nonfatal stroke, all-cause mortality, and heart failure in patients with STEMI. METHODS: We enrolled 1004 patients with STEMI undergoing percutaneous coronary intervention (PCI). Plasma levels of these metabolites were determined by targeted liquid chromatography/mass spectrometry. The associations of metabolite levels with MACEs were assessed with the Cox regression model and quantile g-computation. RESULTS: During a median follow-up of 360 d, 102 patients experienced MACEs. Higher plasma PAGln (hazard ratio [HR], 3.17 [95% CI: 2.05, 4.89]; P < 0.001), IS (2.67 [1.68, 4.24], P < 0.001), DCA (2.36 [1.40, 4.00], P = 0.001), TML (2.66 [1.77,3.99], P < 0.001), and TMAO (2.61 [1.70, 4.00], P < 0.001) levels were significantly associated with MACEs independent of traditional risk factors. According to quantile g-computation, the joint effect of all these metabolites was 1.86 (95% CI: 1.46, 2.27). PAGln, IS and TML had the greatest proportional positive contributions to the mixture effect. Additionally, plasma PAGln and TML combined with coronary angiography scores including the Synergy between PCI with Taxus and cardiac surgery (SYNTAX) score (area under the curve [AUC]: 0.792 vs. 0.673), Gensini score (0.794 vs. 0.647) and Balloon pump-assisted Coronary Intervention Study (BCIS-1) jeopardy score (0.774 vs. 0.573) showed better prediction performance for MACEs. CONCLUSIONS: Higher plasma PAGln, IS, DCA, TML, and TMAO levels are independently associated with MACEs suggesting that these metabolites may be useful markers for prognosis in patients with STEMI.

A Concomitant Cancer Diagnosis Is Associated With Poor Cardiovascular Outcomes Among Acute Myocardial Infarction Patients.

BACKGROUND AND AIMS: With the increasing coexistence of cardiovascular disease and cancer in contemporary clinical practice, studies on the outcomes in acute myocardial infarction (AMI) patients with cancer has not been systematically investigated. This study sought to investigated the effect of coexisting cancer on the treatment and clinical outcomes among AMI patients. METHODS: We retrospectively integrated and analyzed cardiovascular data of 6,607 AMI patients between June 2016 and December 2019. Patients with cancer were compared with pair-matched cancer-naive patients. Cox proportional hazards models were constructed to compare the differences in outcomes. RESULTS: Of 6,607 patients, 2.3% (n = 150) had been diagnosed with cancer. Patients with cancer were older (70.3 ± 10.0 vs. 63.9 ± 11.5 years, P < 0.001) and had a higher burden of comorbidities. Moreover, patients with cancer tended to receive clopidogrel (52.0 vs. 40.0%, P = 0.004) rather than ticagrelor (45.6 vs. 58.2%, P = 0.003) than those without cancer. After pairwise matching, patients with cancer were less likely to undergo in-hospital percutaneous coronary intervention (61.3 vs. 70.0%, P = 0.055). And after 3-year follow-up, the cumulative incidence of cardiovascular death (14.0 vs. 8.3%; adjusted HR, 1.93; 95% CI, 1.11-3.39; P = 0.021) among patients with cancer was significantly higher than that among the matched controls, a similar pattern was observed for the composite outcome of cardiovascular death, non-fatal myocardial infarction, and non-fatal stroke (16.0 vs. 10.3%; adjusted HR, 1.98; 95% CI, 1.21-3.26; P = 0.007). Moreover, patients with a historical cancer diagnosis within 5 years had a higher risk of cardiovascular ischemic events. CONCLUSIONS: AMI patients with a concomitant diagnosis of cancer tended to be treated with conservative therapies and were at substantially higher risk for adverse cardiovascular outcomes.

Association Between Serum Carcinoembryonic Antigen Levels at Different Perioperative Time Points and Colorectal Cancer Outcomes.

BACKGROUND: Whether elevated postoperative serum carcinoembryonic antigen (CEA) levels are prognostic in patients with stage II colorectal cancer (CRC) remains controversial. PATIENTS AND METHODS: Primary and sensitivity analysis populations were obtained from a retrospective, multicenter longitudinal cohort including consecutive patients without neoadjuvant treatment undergoing curative resection for stage I-III CRC. Serum CEA levels before (CEApre-m1) and within 1 (CEApost-m1), 2-3 (CEApost-m2-3), and 4-6 months (CEApost-m4-6) after surgery were obtained, and their associations with recurrence-free survival (RFS) and overall survival (OS) were assessed using Cox regression. Sensitivity and subgroup analyses were performed. RESULTS: Primary and sensitivity analysis populations included 710 [415 men; age, 54.8 (11.6) years] and 1556 patients [941 men; age, 56.2 (11.8) years], respectively. Recurrence hazard ratios (HRs) in the elevated CEApre-m1, CEApost-m1, CEApost-m2-3, and CEApost-m4-6 groups were 1.30 (95% CI: 0.91-1.85), 1.53 (95% CI: 0.89-2.62), 1.88 (95% CI: 1.08-3.28), and 1.15 (95% CI: 0.91-1.85), respectively. The HRs of the elevated CEApre-m1, CEApost-m1, CEApost-m2-3, and CEApost-m4-6 groups for OS were 1.09 (95% CI: 0.60-1.97), 2.78 (95% CI: 1.34-5.79), 2.81 (95% CI: 1.25-6.30), and 3.30 (95% CI: 1.67-.536), respectively. Adjusted multivariate analyses showed that both in the primary and sensitivity analysis populations, elevated CEApost-m2-3, rather than CEApre-m1, CEApost-m1, and CEApost-m4-6, was an independent risk factor for recurrence, but not for OS. The RFS in the elevated and normal CEApost-m2-3 groups differed significantly among patients with stage II disease [n = 266; HR, 2.89; 95% CI, 1.02-8.24 (primary analysis); n = 612; HR, 2.69; 95% CI, 1.34-5.38 (sensitivity analysis)]. CONCLUSIONS: Elevated postoperative CEA levels are prognostic in patients with stage II CRC, with 2-3 months after surgery being the optimal timing for CEA measurement.

Metabolic phenotyping to monitor chronic enteritis canceration.

INTRODUCTION: Colorectal cancer (CRC) remains an incurable disease. Previous metabolomic studies show that metabolic signatures in plasma distinguish CRC patients from healthy controls. Chronic enteritis (CE) represents a risk factor for CRC, with a 20 fold greater incidence than in healthy individuals. However, no studies have performed metabolomic profiling to investigate CRC biomarkers in CE. OBJECTIVE: Our aims were to identify metabolomic signatures in CRC and CE and to search for blood-derived metabolite biomarkers distinguishing CRC from CE, especially early-stage biomarkers. METHODS: In this case-control study, 612 subjects were prospectively recruited between May 2015 and May 2016, and including 539 CRC patients (stage I, 102 cases; stage II, 259 cases; stage III, 178 cases) and 73 CE patients. Untargeted metabolomics was performed to identify CRC-related metabolic signatures in CE. RESULTS: Five pathways were significantly enriched based on 153 differential metabolites between CRC and CE. 16 biomarkers were identified for diagnosis of CRC from CE and for guiding CRC staging. The AUC value for CRC diagnosis in the external validation set was 0.85. Good diagnostic performances were also achieved for early-stage CRC (stage I and stage II), with an AUC value of 0.84. The biomarker panel could also stage CRC patients, with an AUC of 0.72 distinguishing stage I from stage II CRC and AUC of 0.74 distinguishing stage II from stage III CRC. CONCLUSIONS: The identified metabolic biomarkers exhibit promising properties for CRC monitoring in CE patients and are superior to commonly used clinical biomarkers (CEA and CA19-9).

Correlations Between Ultrasonographic Findings of Invasive Lobular Carcinoma of the Breast and Intrinsic Subtypes.

PURPOSE: To analyze the ultrasonographic findings of invasive lobular carcinoma (ILC) of the breast in 360 women and the correlations between the characteristics and the intrinsic subtypes. MATERIALS AND METHODS: We evaluated the imaging findings according to the lexicon of the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS). The included ultrasonographic features were shape, orientation, margin, echo pattern, posterior features, calcifications, the vascularity of the masses and the presence of architectural distortions. The associations between those features and the intrinsic ILC subtypes were investigated. RESULTS: The most common manifestations of ILC on ultrasound (US) were hypoechoic masses with irregular shape, parallel orientation, spiculated margin, posterior acoustic shadowing, no calcification and blood vessels in the rim. The patients in the luminal A subtype were the youngest, and the patients in the HER2 overexpression subtype were the oldest (p = 0.01). On US, the HER2 overexpression subtype was characterized by microlobulated margins (p = 0.002), while the luminal A subtype and the luminal B subtype mostly had spiculated margins. The basal-like subtype was distinctive in that it had no posterior features (p = 0.041), rather than acoustic shadowing, and the masses in the HER2 and basal-like subtypes were larger than in the other two groups (p = 0.03). CONCLUSION: There were significant differences and several trends in the ultrasonographic characteristics of different intrinsic subtypes, which may supply accurate imaging diagnostic criteria to assist in the management of individuals with ILC.

Development of a Correlative Strategy To Discover Colorectal Tumor Tissue Derived Metabolite Biomarkers in Plasma Using Untargeted Metabolomics.

The metabolic profiling of biofluids using untargeted metabolomics provides a promising choice to discover metabolite biomarkers for clinical cancer diagnosis. However, metabolite biomarkers discovered in biofluids may not necessarily reflect the pathological status of tumor tissue, which makes these biomarkers difficult to reproduce. In this study, we developed a new analysis strategy by integrating the univariate and multivariate correlation analysis approach to discover tumor tissue derived (TTD) metabolites in plasma samples. Specifically, untargeted metabolomics was first used to profile a set of paired tissue and plasma samples from 34 colorectal cancer (CRC) patients. Next, univariate correlation analysis was used to select correlative metabolite pairs between tissue and plasma, and a random forest regression model was utilized to define 243 TTD metabolites in plasma samples. The TTD metabolites in CRC plasma were demonstrated to accurately reflect the pathological status of tumor tissue and have great potential for metabolite biomarker discovery. Accordingly, we conducted a clinical study using a set of 146 plasma samples from CRC patients and gender-matched polyp controls to discover metabolite biomarkers from TTD metabolites. As a result, eight metabolites were selected as potential biomarkers for CRC diagnosis with high sensitivity and specificity. For CRC patients after surgery, the survival risk score defined by metabolite biomarkers also performed well in predicting overall survival time ( p = 0.022) and progression-free survival time ( p = 0.002). In conclusion, we developed a new analysis strategy which effectively discovers tumor tissue related metabolite biomarkers in plasma for cancer diagnosis and prognosis.

An Immune-Related Signature Predicts Survival in Patients With Lung Adenocarcinoma.

We investigated the local immune status and its prognostic value in lung adenocarcinoma. In total, 513 lung adenocarcinoma samples from TCGA and ImmPort databases were collected and analyzed. The R package coxph was employed to mine immune-related genes that were significant prognostic indicators using both univariate and multivariate analyses. The R software package glmnet was then used for Lasso Cox regression analysis, and a prognosis prediction model was constructed for lung adenocarcinoma; clusterProfiler was selected for functional gene annotations and KEGG enrichment analysis. Finally, correlations between the RiskScore and clinical features or signaling pathways were established. Sixty-four immune-related genes remarkably correlated with patient prognosis and were further applied. Samples were hierarchically clustered into two subgroups. Accordingly, the LASSO regression algorithm was employed to screen the 14 most representative immune-related genes (PSMD11, PPIA, MIF, BMP5, DKK1, PDGFB, ANGPTL4, IL1R2, THRB, LTBR, TNFRSF1, TNFRSF17, IL20RB, and MC1R) with respect to patient prognosis. Then, the prognosis prediction model for lung adenocarcinoma patients (namely, the RiskScore equation) was constructed, and the training set samples were incorporated to evaluate the efficiency of this model to predict and classify patient prognosis. Subsequently, based on functional annotations and KEGG pathway analysis, the 14 immune-related genes were mainly enriched in pathways closely associated with lung adenocarcinoma and its immune microenvironment, such as cytokine-cytokine receptor interaction and human T-cell leukemia virus 1 infection. Furthermore, correlations between the RiskScore and clinical features of the training set samples and signaling pathways (such as p53, cell cycle, and DNA repair) were also demonstrated. Finally, the test set sample data were employed for independent testing and verifying the model. We established a prognostic prediction RiskScore model based on the expression profiles of 14 immune-related genes, which shows high prediction accuracy and stability in identifying immune features. This could provide clinical guidance for the diagnosis and prognosis of different immunophenotypes, and suggest multiple targets for precise advanced lung adenocarcinoma therapy based on subtype-specific immune molecules.

Plasma metabolomic profiling distinguishes right-sided from left-sided colon cancer.

BACKGROUND: Many studies have demonstrated that right-sided colon cancer (RCC) has a higher mortality rate and worse prognosis than left-sided colon cancer (LCC). However, the underlying biological mechanism that can account for these differences is unclear. METHODS: In this study, plasma metabolic profiles in 147 LCC patients and 105 RCC patients were systematically analyzed by the ultra high performance liquid chromatography quadruple time-of-flight mass spectrometry (UHPLC-QTOF/MS) platform in conjunction with univariate and multivariate statistical analysis. RESULTS: Metabolic signatures revealed considerable differences between patients with RCC and LCC, and clear separations were observed between the two groups in partial least-squares discriminant analysis score plots. In total, six metabolites were identified as potential metabolite markers for tumor location in RCC compared with LCC, including upregulated trimethylamine N-oxide and indoxyl sulfate, and downregulated anserine, L-targinine, gamma-glutamyl-gamma-aminobutyraldehyde and pyridoxal 5'-phosphate. These differences highlight that significant alternations occur in the pathways of methane metabolism, arginine and proline metabolism, histidine metabolism, beta-alanine metabolism and vitamin B6 metabolism in RCC compared with LCC. CONCLUSIONS: Identified biomarkers and metabolic pathways may facilate our understanding of the different mortality rates and prognoses between RCC and LCC.

SWATHtoMRM: Development of High-Coverage Targeted Metabolomics Method Using SWATH Technology for Biomarker Discovery.

The complexity of metabolome presents a great analytical challenge for quantitative metabolite profiling, and restricts the application of metabolomics in biomarker discovery. Targeted metabolomics using multiple-reaction monitoring (MRM) technique has excellent capability for quantitative analysis, but suffers from the limited metabolite coverage. To address this challenge, we developed a new strategy, namely, SWATHtoMRM, which utilizes the broad coverage of SWATH-MS technology to develop high-coverage targeted metabolomics method. Specifically, SWATH-MS technique was first utilized to untargeted profile one pooled biological sample and to acquire the MS2 spectra for all metabolites. Then, SWATHtoMRM was used to extract the large-scale MRM transitions for targeted analysis with coverage as high as 1000-2000 metabolites. Then, we demonstrated the advantages of SWATHtoMRM method in quantitative analysis such as coverage, reproducibility, sensitivity, and dynamic range. Finally, we applied our SWATHtoMRM approach to discover potential metabolite biomarkers for colorectal cancer (CRC) diagnosis. A high-coverage targeted metabolomics method with 1303 metabolites in one injection was developed to profile colorectal cancer tissues from CRC patients. A total of 20 potential metabolite biomarkers were discovered and validated for CRC diagnosis. In plasma samples from CRC patients, 17 out of 20 potential biomarkers were further validated to be associated with tumor resection, which may have a great potential in assessing the prognosis of CRC patients after tumor resection. Together, the SWATHtoMRM strategy provides a new way to develop high-coverage targeted metabolomics method, and facilitates the application of targeted metabolomics in disease biomarker discovery. The SWATHtoMRM program is freely available on the Internet ( http://www.zhulab.cn/software.php ).

MicroRNA-144 functions as a tumor suppressor in gastric cancer by targeting cyclooxygenase-2.

Gastric cancer (GC) poses a serious public health threat and the 5-year survival rate of patients with GC is low. MicroRNAs (miRNAs/miRs) may serve oncogenic or tumor suppressor functions during tumorigenesis by regulating cell proliferation, apoptosis, migration and invasion and it has been demonstrated that they may be dysregulated in various types of cancer. The present study demonstrated that miR-144 and GATA4 were downregulated in GC tissues and cell lines and suggested that this may be due to hypermethylation. Additionally, miR-144 and GATA4 had synergistic effects on GC cells by repressing cell proliferation and inducing cell cycle arrest and apoptosis. The results of bioinformatics and a luciferase reporter assay indicated that cyclooxygenase-2 (COX-2) is a direct target of miR-144 and that miR-144 negatively regulated the expression of COX-2, which inhibits the viability of GC cells. GATA4 also induced a similar effect on COX-2. Taken together, the results of the present study may improve understanding of the underlying mechanism of miR-144 and GATA4 in GC.

Metabolomics approach for predicting response to neoadjuvant chemotherapy for colorectal cancer.

INTRODUCTION: Colorectal cancer (CRC) is a clinically heterogeneous disease, which necessitates a variety of treatments and leads to different outcomes. Only some CRC patients will benefit from neoadjuvant chemotherapy (NACT). OBJECTIVES: An accurate prediction of response to NACT in CRC patients would greatly facilitate optimal personalized management, which could improve their long-term survival and clinical outcomes. METHODS: In this study, plasma metabolite profiling was performed to identify potential biomarker candidates that can predict response to NACT for CRC. Metabolic profiles of plasma from non-response (n = 30) and response (n = 27) patients to NACT were studied using UHPLC-quadruple time-of-flight)/mass spectrometry analyses and statistical analysis methods. RESULTS: The concentrations of nine metabolites were significantly different when comparing response to NACT. The area under the receiver operating characteristic curve value of the potential biomarkers was up to 0.83 discriminating the non-response and response group to NACT, superior to the clinical parameters (carcinoembryonic antigen and carbohydrate antigen 199). CONCLUSION: These results show promise for larger studies that could result in more personalized treatment protocols for CRC patients.

Associating liver partition and portal vein ligation for staged hepatectomy versus conventional staged hepatectomy: a meta-analysis.

INTRODUCTION: Controversies persist between associating liver partition and portal vein ligation for staged hepatectomy (ALPPS) and conventional staged hepatectomy. This meta-analysis aims to compare completion, regeneration capacity, and surgical outcomes between the two strategies. EVIDENCE ACQUISITION: We systematically searched PubMed, EMBASE, Cochrane Library, Medline. The main endpoints consisted of completion rate, future liver remnant (FLR) hypertrophy ratio, morbidity, major complication, minor complication, post-hepatectomy liver failure (PHLF) and mortality. Pooled data was assessed by the use of a random-effects model. Odds ratios (OR) were calculated for dichotomous outcomes and mean differences (MD) for continuous outcomes. EVIDENCE SYNTHESIS: Of the 124 identified studies, 7 were eligible and were included in our analysis (N.=525 participants). In the two groups, there was no statistical difference in morbidity (OR=1.62; 95% CI: 0.81-3.20; Z=1.37; P=0.17), minor complication rate (OR=1.27; 95% CI: 0.50-3.21; Z=0.51; P=0.61), PHLF rate (OR=0.87; 95% CI: 0.34-2.22; Z=0.30; P=0.76), mortality (OR=1.68; 95% CI: 0.59-4.83; Z=0.97; P=0.33). Meanwhile, statistical significance was showed in the completion rate (OR=8.29; 95% CI: 2.49-27.53; Z=3.45; P=0.0006), FLR hypertrophy ratio (MD=28.00; 95% CI: 16.06-39.93; Z=4.60; P<0.00001) and major complication rate (OR=1.83; 95% CI: 1.08-3.10; Z=2.26; P=0.02). CONCLUSIONS: Compared with conventional staged hepatectomy, ALPPS provides a higher completion rate and FLR hypertrophy ratio. However, it results in more major complications. Conventional staged hepatectomy is not better than ALPPS in the aspects of minor complication, PHLF, morbidity and mortality.

Tumor LDH-A expression and serum LDH status are two metabolic predictors for triple negative breast cancer brain metastasis.

There are limited therapeutic methods for triple negative breast cancer in the clinic, which is easy to progress into the brain to form metastatic lesions and evolve into the terminal stage. Because both the primary cancer and the brain metastasis have high glycolysis, we hypothesize that lactate dehydrogenase (LDH), which catalyzes the final step of glycolysis, may be a predictor, as well as a treatment target, for breast cancer brain metastasis. Therefore, the expression of LDH-A was detected on 119 triple negative breast cancer tissues with immunohistochemistry, and the serum LDH levels were also measured. Our results showed that the LDH-A expression inside the tumor was significantly higher than the matched normal tissues. Tumor LDH-A expression, serum LDH status, and the slope of serum LDH status were closely associated with triple negative breast cancer brain metastasis and brain metastasis free survival. This study indicates that tumor LDH and serum LDH status are two predictors for triple negative breast cancer brain metastasis.

Metabolomics for biomarker discovery in the diagnosis, prognosis, survival and recurrence of colorectal cancer: a systematic review.

Colorectal cancer (CRC) remains an incurable disease. There are no effective noninvasive techniques that have achieved colorectal cancer (CRC) diagnosis, prognosis, survival and recurrence in clinic. To investigate colorectal cancer metabolism, we perform an electronic literature search, from 1998 to January 2016, for studies evaluating the metabolomic profile of patients with CRC regarding the diagnosis, recurrence, prognosis/survival, and systematically review the twenty-three literatures included. QUADOMICS tool was used to assess the quality of them. We highlighted the metabolism perturbations based on metabolites and pathway. Metabolites related to cellular respiration, carbohydrate, lipid, protein and nucleotide metabolism were significantly altered in CRC. Altered metabolites were also related to prognosis, survival and recurrence of CRC. This review could represent the most comprehensive information and summary about CRC metabolism to date. It certificates that metabolomics had great potential on both discovering clinical biomarkers and elucidating previously unknown mechanisms of CRC pathogenesis.