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Browsing by Author "Wei, Siwei"
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Item Differentiating Hepatocellular Carcinoma from Hepatitis C Using Metabolite Profiling(MDPI, 2012-10-10) Wei, Siwei; Suryani, Yuliana; Gowda, G. A. Nagana; Skill, Nicholas; Maluccio, Mary; Raftery, Daniel; Surgery, School of MedicineHepatocellular carcinoma (HCC) accounts for most liver cancer cases worldwide. Contraction of the hepatitis C virus (HCV) is considered a major risk factor for liver cancer. In order to identify the risk of cancer, metabolic profiling of serum samples from patients with HCC (n=40) and HCV (n=22) was performed by 1H nuclear magnetic resonance spectroscopy. Multivariate statistical analysis showed a distinct separation of the two patient cohorts, indicating a distinct metabolic difference between HCC and HCV patient groups based on signals from lipids and other individual metabolites. Univariate analysis showed that three metabolites (choline, valine and creatinine) were significantly altered in HCC. A PLS-DA model based on these three metabolites showed a sensitivity of 80%, specificity of 71% and an area under the receiver operating curve of 0.83, outperforming the clinical marker alpha-fetoprotein (AFP). The robustness of the model was tested using Monte-Carlo cross validation (MCCV). This study showed that metabolite profiling could provide an alternative approach for HCC screening in HCV patients, many of whom have high risk for developing liver cancer.Item Metabolomics approach for predicting response to neoadjuvant chemotherapy for breast cancer(Wiley, 2013-06) Wei, Siwei; Liu, Lingyan; Zhang, Jian; Bowers, Jeremiah; Gowda, G.A. Nagana; Seeger, Harald; Fehm, Tanja; Neubauer, Hans J.; Vogel, Ulrich; Clare, Susan E.; Raftery, Daniel; Surgery, School of MedicineBreast cancer is a clinically heterogeneous disease, which necessitates a variety of treatments and leads to different outcomes. As an example, only some women will benefit from chemotherapy. Identifying patients who will respond to chemotherapy and thereby improve their long‐term survival has important implications to treatment protocols and outcomes, while identifying non responders may enable these patients to avail themselves of other investigational approaches or other potentially effective treatments. In this study, serum metabolite profiling was performed to identify potential biomarker candidates that can predict response to neoadjuvant chemotherapy for breast cancer. Metabolic profiles of serum from patients with complete (n = 8), partial (n = 14) and no response (n = 6) to chemotherapy were studied using a combination of nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography–mass spectrometry (LC–MS) and statistical analysis methods. The concentrations of four metabolites, three (threonine, isoleucine, glutamine) from NMR and one (linolenic acid) from LC–MS were significantly different when comparing response to chemotherapy. A prediction model developed by combining NMR and MS derived metabolites correctly identified 80% of the patients whose tumors did not show complete response to chemotherapy. These results show promise for larger studies that could result in more personalized treatment protocols for breast cancer patients., ► Metabolomics differentiates response to neoadjuvant breast cancer chemotherapy.► Four serum metabolites are found to correlate with response to chemotherapy.► A 4‐metabolite model identifies 80% of the patients not showing complete response.► Additional studies on larger patient cohorts are needed to validate the findings.