Noninvasive predictive models based on lifestyle analysis and risk factors for early-onset colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) incidence has increased among patients aged <50 years. Exploring high-risk factors and screening high-risk populations may help lower early-onset CRC (EO-CRC) incidence. We developed noninvasive predictive models for EO-CRC and investigated its risk factors. METHODS: This retrospective multicenter study collected information on 1756 patients (811 patients with EO-CRC and 945 healthy controls) from two medical centers in China. Sociodemographic features, clinical symptoms, medical and family history, lifestyle, and dietary factors were measured. Patients from one cohort were randomly assigned (8:2) to two groups for model establishment and internal validation, and another independent cohort was used for external validation. Multivariable logistic regression, random forest, and eXtreme Gradient Boosting (XGBoost) were performed to establish noninvasive predictive models for EO-CRC. Some variables in the model influenced EO-CRC occurrence and were further analyzed. Multivariable logistic regression analysis yielded adjusted odd ratios (ORs) and 95% confidence intervals (CIs). RESULTS: All three models showed good performance, with areas under the receiver operator characteristic curves (AUCs) of 0.82, 0.84, and 0.82 in the internal and 0.78, 0.79, and 0.78 in the external validation cohorts, respectively. Consumption of sweet (OR 2.70, 95% CI 1.89-3.86, P < 0.001) and fried (OR 2.16, 95% CI 1.29-3.62, P < 0.001) foods ≥3 times per week was significantly associated with EO-CRC occurrence. CONCLUSION: We established noninvasive predictive models for EO-CRC and identified multiple nongenetic risk factors, especially sweet and fried foods. The model has good performance and can help predict the occurrence of EO-CRC in the Chinese population.

A metabonomic approach to chemosensitivity prediction of cisplatin plus 5-fluorouracil in a human xenograft model of gastric cancer.

The prediction of chemosensitivity is a challenging problem in the management of cancer. In the present study, a metabonomic approach was proposed to assess the feasibility of chemosensitivity prediction in a human xenograft model of gastric cancer. BALB/c-nu/nu mice were transplanted with MKN-45 cell line to establish the xenograft model. The mice were then randomized into treatment group (cisplatin and 5-fluorouracil) and control group (0.9% sodium chloride), and their plasma were collected before treatment. Metabolic profiles of all plasma samples were acquired by using high-performance liquid chromatography coupled with a quadrupole time-of-flight mass spectrometer (HPLC/Q-TOF-MS). Based on the data of metabolic profiles and k-Nearest Neighbor algorithm, a prediction model for chemosensitivity was developed and an average accuracy of 90.4% was achieved. In addition, a series of endogenous metabolites, including 1-acyl-lysophosphatidycholines, polyunsaturated fatty acids and their derivatives, were determined as potential indicators of chemosensitivity. In conclusion, our results suggest that the proposed metabonomic approach allows effective chemosensitivity prediction in human xenograft model of gastric cancer. The approach presents a new concept in the chemosensitivtiy prediction of cancer and is expected to be developed as a powerful tool in the personalized cancer therapy.

Prediction of hepatic metastasis and relapse in colorectal cancers based on concordance analyses with liver fibrosis scores.

BACKGROUND: Liver fibrosis, resulted from several liver diseases, are increasing up to 25% in population in global. It remains undetermined how much impact liver fibrosis have on the development of hepatic metastasis and relapse in colorectal cancer (CRC). Hence the aim of this study was to clarify the role of liver fibrosis on hepatic metastasis and relapse in CRC undergoing curative therapy. METHODS: We enrolled consecutive 1652 patients with radical colorectal surgery as the discovery cohort, and the validation set enrolled 432 CRC patients with hepatic metastasis. To determine liver fibrosis, the NFS, FIB4 and APRI scores were applied. The influence of liver fibrosis on hepatic metastasis and relapse was assessed by survival analyses. Nomograms with fibrosis score incorporated were established to identify the incremental value for individualized relapse estimation, which was then assessed with respect to calibration, discrimination, and clinical usefulness. RESULTS: The high liver fibrosis score patients had significantly worse outcomes than low score in 5-year hepatic metastasis (22.6 vs. 8.7%) in discovery cohort, and relapse (58.2 vs. 44.1%) in validation cohort. Multivariate analysis also revealed liver fibrosis as an independent prognostic factor. The distribution analysis also demonstrated higher liver fibrosis score a powerful prognostic factor for hepatic metastasis and relapse. The nomogram incorporated with liver fibrosis score resulted in better performance than TNM staging system and clinicopathologic nomograms. Importantly, the discriminatory capacity of the fibrosis score was superior to that of the CRS score in predicting hepatic specific disease-free survival (DFS) and relapse-free survival (RFS), as demonstrated by the C-index and AUC. The concordance study showed well agreement among NFS, FIB4 and APRI in predicting DFS and RFS. Among these three noninvasive liver fibrosis scores, NFS score performed the best in predicting hepatic specific DFS and RFS. CONCLUSION: The liver fibrosis was a powerful predictor of hepatic specific DFS and RFS in CRC. Fibrosis niche may be a favorable microenvironment for metastatic formation in the liver.

Development and validation of a robust multigene signature as an aid to predict early relapse in stage I-III clear cell and papillary renal cell cancer.

Background and objectives: Multi-gene signature can be used as prognostic indicator in many types of cancer, but the association with early-relapse in patients with stage I-III clear cell and papillary renal cell cancer (RCC) is unknown. We aim to establish a mRNAs signature for improving prediction of early-relapse in patients with stage I-III clear cell and papillary RCC. Methods: The data of 610 patients with stage I-III RCC from The Cancer Genome Atlas (TCGA) and 270 patients from Fudan University Shanghai Cancer Center (FUSCC) were extracted. Propensity score matching analysis, linear models for microarray data VOOM method, least absolute shrinkage and selection operation Cox regression modeling analysis was conducted in turn for selecting multi-mRNA signature. Survival differences were assessed by Kaplan-Meier estimate and compared using log-rank test. Multivariable Cox regression and time-dependent receiver operating characteristic curves were used to evaluate the association of mRNAs signature with relapse-free survival (RFS). Results: Seventeen mRNAs were identified to constitute the early-relapse signature. Among patients with stage I-III RCC, those with high-risk score calculated from 17 mRNAs signature showed shorter RFS than those with low-risk score, both in TCGA discovery and internal validation sets, and in FUSCC discovery and internal validation sets (all p < 0.05). In multivariable Cox regression analysis, the 17 mRNAs signature remained an independent prognostic factor both in TCGA discovery (HR 2.43, 95%CI 1.98-2.96) and internal validation sets (HR 1.66, 95%CI 1.19-2.30), and FUSCC discovery (HR 1.28, 95%CI 1.13-1.43) and internal validation sets (HR 1.65, 95%CI 1.11-2.48). Additionally, the 17 mRNAs signature achieved a higher accuracy for RFS estimation beyond clinical indicator. Conclusion: The 17 mRNAs signature could classify stage I-III RCC patients into low- or high-risk of early-relapse, and will help to guide interventions to optimize survival outcomes.

A metabonomic approach to early prognostic evaluation of experimental sepsis by (1)H NMR and pattern recognition.

This study proposes an NMR-based metabonomic approach to early prognostic evaluation of sepsis. Forty septic rats receiving cecal ligation and puncture (CLP) were divided into the surviving group and nonsurviving group on day 6, while 20 sham-operated rats served as the control group. Serum samples were collected from septic and sham-operated rats at 12 h after surgery and analyzed using (1)H NMR spectroscopy. Orthogonal partial least squares (OPLS) were applied and showed clustering according to predefined groups, indicating that NMR-based metabolic profiling could reveal pathologic characteristics in the serum of sham-operated, surviving, and nonsurviving septic rats. In addition, six characteristic metabolites including lactate, alanine, acetate, acetoacetate, hydroxybutyrate, and formate, which are mainly involved in energy metabolism, changed markedly in septic rats, especially in the nonsurvivors. Using these metabolites, a predictive model for prognostic evaluation of sepsis was constructed using a radial basis function neural network (RBFNN) with a prediction accuracy of about 87% by test samples. The results indicated that the NMR-based metabonomic approach is a potential technique for the early prognostic evaluation of sepsis.

[Applications of metabonomics in clinical diagnosis].

Metabonomics, as a newly developed technique, is expected to be a powerful technique in clinical diagnosis. Metabonomics-based diagnosis involves the global metabolic analysis of body-fluids, determination of biomarkers by multivariate statistic analysis, and establishemen of mathematic models for clinical diagnosis with the aid of pattern recognition. This article reviews the adoption of various analytical and computational strategies, application of metabonomics in clinical diagnosis, and potential challenges and development trends.

Characterization of chlorinated valepotriates from Valeriana jatamansi.

HPLC-PDA-MS and TLC analysis were used to look for minor cytotoxic chlorinated valepotriates from whole plants of Valeriana jatamansi (syn. Valeriana wallichii DC.). This resulted in isolation of 15 chlorinated valepotriates, designated as chlorovaltrates A-O, together with six known analogues, (1S,3R,5R,7S,8S,9S)-3,8-epoxy-1,5-dihydroxyvalechlorine, volvaltrate B, chlorovaltrate, rupesin B, (1S,3R,5R,7S,8S,9S)-3,8-epoxy-1-O-ethyl-5-hydroxyvalechlorine, and (1R,3R,5R,7S,8S,9S)-3,8-epoxy-1-O-ethyl-5-hydroxyvalechlorine. Their structures were elucidated by spectroscopic methods including homo- and heteronuclear two-dimensional NMR experiments. Chlorovaltrates K-N, chlorovaltrate and rupesin B showed moderate cytotoxicity against lung adenocarcinoma (A 549), metastatic prostate cancer (PC-3M), colon cancer (HCT-8) and hepatoma (Bel 7402) cell lines with IC50 values of 0.89-9.76 µM.

Chemometrics-based approach to modeling quantitative composition-activity relationships for Radix Tinosporae.

Quantitative composition-activity relationship (QCAR) study makes it possible to discover active components in traditional Chinese medicine (TCM) and to predict the integral bioactivity by its chemical composition. In the study, 28 samples of Radix Tinosporae were quantitatively analyzed by high performance liquid chromatography, and their analgesic activities were investigated via abdominal writhing tests on mice. Three genetic algorithms (GA) based approaches including partial least square regression, radial basis function neural network, and support vector regression (SVR) were established to construct QCAR models of R. Tinosporae. The result shows that GA-SVR has the best model performance in the bioactivity prediction of R. Tinosporae; seven major components thereof were discovered to have analgesic activities, and the analgesic activities of these components were partly confirmed by subsequent abdominal writhing test. The proposed approach allows discovering active components in TCM and predicting bioactivity by its chemical composition, and is expected to be utilized as a supplementary tool for the quality control and drug discovery of TCM.