Decreased P27 protein expression is correlated with the progression and poor prognosis of nasopharyngeal carcinoma

Background

To determine the correlation of cyclin-dependent kinase inhibitor 1B (p27) expression with clinicopathologic features in nasopharyngeal carcinoma (NPC), including patient prognosis.

Methods

Real-time PCR and immunohistochemistry were used to examine the mRNA and protein expressions of p27 in NPC and nasopharyngeal tissues. The relationship of p27 expression levels with clinical features and prognosis of NPC patients was analyzed.

Results

The expression level of p27 mRNA was markedly lower in NPC tissues than that in the nasopharyngeal tissues (P?=?0.0006). Specific p27 protein staining by immunohistochemistry was found in the nuclei and cytoplasm of nasopharyngeal and malignant epithelial cells but decreased expression was observed in NPC samples compared to normal epithelium samples (P?=?0.002). In addition, low levels of p27 protein were inversely correlated with the status of T classification (p?=?0.002) and clinical stage (p?=?0.019) of NPC patients. Patients with lower p27 expression had a significantly shorter overall survival time than did patients with high p27 expression. Multivariate analysis suggested that the level of p27 expression was not an independent prognostic indicator (p?=?0.682) for NPC survival.

Conclusion

Low level of p27 expression is a potential unfavorable prognostic factor for patients with NPC.

Virtual slides

The virtual slide (s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1915282782109343.

     

Microarray-based analysis and clinical validation identify ubiquitin-conjugating enzyme E2E1 (<Emphasis Type="Italic">UBE2E1</Emphasis>) as a prognostic factor in acute myeloid leukemia

Background

Previous research suggested that single gene expression might be correlated with acute myeloid leukemia (AML) survival. Therefore, we conducted a systematical analysis for AML prognostic gene expressions.

Methods

We performed a microarray-based analysis for correlations between gene expression and adult AML overall survival (OS) using datasets GSE12417 and GSE8970. Positive findings were validated in an independent cohort of 50 newly diagnosed, non-acute promyelocytic leukemia (APL) AML patients by quantitative RT-PCR and survival analysis.

Results

Microarray-based analysis suggested that expression of eight genes was each associated with 1-year and 3-year AML OS in both GSE12417 and GSE8970 datasets (p?<?0.05). Next, we validated our findings in an independent cohort of AML samples collected in our hospital. We found that ubiquitin-conjugating enzyme E2E1 (UBE2E1) expression was adversely correlated with AML survival (p?=?0.04). Multivariable analysis showed that UBE2E1 ^high patients had a significant shorter OS and shorter progression-free survival after adjusting other known prognostic factors (p?=?0.03). At last, we found that UBE2E1 expression was negatively correlated with patients’ response to induction chemotherapy (p?<?0.05).

Conclusions

In summary, we demonstrated that UBE2E1 expression was a novel prognostic factor in adult, non-APL AML patients.

     

Upregulation of the solute carrier family 7 genes is indicative of poor prognosis in papillary thyroid carcinoma

Background

The solute carrier (SLC) 7 family genes comprise 14 members and function as cationic amino acid/glycoprotein transporters in many cells, they are essential for the maintenance of amino acid nutrition and survival of tumor cells. This study was conducted to analyze the associations of SLC7 family gene expression with mortality in papillary thyroid carcinoma (PTC).

Methods

Clinical features, somatic mutations, and SLC7 family gene expression data were downloaded from The Cancer Genome Atlas database. Linear regression model analysis was performed to analyze the correlations between SLC7 family gene expression and clinicopathologic features. Kaplan-Meier survival and logistic regression analyses were performed to characterize the associations between gene expression and patients’ overall survival.

Results

Patient mortality was negatively associated with age and tumor size but positively increased cancer stage and absence of thyroiditis in PTC patients. Kaplan-Meier survival analysis indicated that patients with high SLC7A3, SLC7A5, and SLC7A11 expression levels exhibited poorer survival than those with low SLC7A3, SLC7A5, and SLC7A11 expression levels (P?<?0.05 for all cases). Logistic regression analysis showed that SLC7A3, SLC7A5, and SLC7A11 were associated with increased mortality (odds ratio [OR] 8.61, 95% confidence interval [CI] 2.3–55.91; OR 3.87, 95% CI 1.18–17.31; and OR 3.87, 95% CI 1.18–17.31, respectively.

Conclusion

Upregulation of SLC7A3, SLC7A5, and SLC7A11 expression was associated with poor prognosis in PTC patients, and SLC7 gene expression levels are potentially useful prognostic biomarkers.

     

Monitoring cancer prognosis,diagnosis and treatment efficacy using metabolomics and lipidomics

Introduction

Cellular metabolism is altered during cancer initiation and progression, which allows cancer cells to increase anabolic synthesis, avoid apoptosis and adapt to low nutrient and oxygen availability. The metabolic nature of cancer enables patient cancer status to be monitored by metabolomics and lipidomics. Additionally, monitoring metabolic status of patients or biological models can be used to greater understand the action of anticancer therapeutics.

Objectives

Discuss how metabolomics and lipidomics can be used to (i) identify metabolic biomarkers of cancer and (ii) understand the mechanism-of-action of anticancer therapies. Discuss considerations that can maximize the clinical value of metabolic cancer biomarkers including case–control, prognostic and longitudinal study designs.

Methods

A literature search of the current relevant primary research was performed.

Results

Metabolomics and lipidomics can identify metabolic signatures that associate with cancer diagnosis, prognosis and disease progression. Discriminatory metabolites were most commonly linked to lipid or energy metabolism. Case–control studies outnumbered prognostic and longitudinal approaches. Prognostic studies were able to correlate metabolic features with future cancer risk, whereas longitudinal studies were most effective for studying cancer progression. Metabolomics and lipidomics can help to understand the mechanism-of-action of anticancer therapeutics and mechanisms of drug resistance.

Conclusion

Metabolomics and lipidomics can be used to identify biomarkers associated with cancer and to better understand anticancer therapies.

     

Predicting ovarian cancer recurrence by plasma metabolic profiles before and after surgery

Background

Previous metabolomic studies have revealed that plasma metabolic signatures may predict epithelial ovarian cancer (EOC) recurrence. However, few studies have performed metabolic profiling of pre- and post-operative specimens to investigate EOC prognostic biomarkers.

Objective

The aims of our study were to compare the predictive performance of pre- and post-operative specimens and to create a better model for recurrence by combining biomarkers from both metabolic signatures.

Methods

Thirty-five paired plasma samples were collected from 35 EOC patients before and after surgery. The patients were followed-up until December, 2016 to obtain recurrence information. Metabolomics using rapid resolution liquid chromatography–mass spectrometry was performed to identify metabolic signatures related to EOC recurrence. The support vector machine model was employed to predict EOC recurrence using identified biomarkers.

Results

Global metabolomic profiles distinguished recurrent from non-recurrent EOC using both pre- and post-operative plasma. Ten common significant biomarkers, hydroxyphenyllactic acid, uric acid, creatinine, lysine, 3-(3,5-diiodo-4-hydroxyphenyl) lactate, phosphohydroxypyruvic acid, carnitine, coproporphyrinogen, l-beta-aspartyl-l-glutamic acid and 24,25-hydroxyvitamin D3, were identified as predictive biomarkers for EOC recurrence. The area under the receiver operating characteristic (AUC) values in pre- and post-operative plasma were 0.815 and 0.909, respectively; the AUC value after combining the two sets reached 0.964.

Conclusion

Plasma metabolomic analysis could be used to predict EOC recurrence. While post-operative biomarkers have a predictive advantage over pre-operative biomarkers, combining pre- and post-operative biomarkers showed the best predictive performance and has great potential for predicting recurrent EOC.

     

Integrated analysis of microRNA-target interactions with clinical outcomes for cancers

Background

Clinical statement alone is not enough to predict the progression of disease. Instead, the gene expression profiles have been widely used to forecast clinical outcomes. Many genes related to survival have been identified, and recently miRNA expression signatures predicting patient survival have been also investigated for several cancers. However, miRNAs and their target genes associated with clinical outcomes have remained largely unexplored.

Methods

Here, we demonstrate a survival analysis based on the regulatory relationships of miRNAs and their target genes. The patient survivals for the two major cancers, ovarian cancer and glioblastoma multiforme (GBM), are investigated through the integrated analysis of miRNA-mRNA interaction pairs.

Results

We found that there is a larger survival difference between two patient groups with an inversely correlated expression profile of miRNA and mRNA. It supports the idea that signatures of miRNAs and their targets related to cancer progression can be detected via this approach.

Conclusions

This integrated analysis can help to discover coordinated expression signatures of miRNAs and their target mRNAs that can be employed for therapeutics in human cancers.

     

Metabolic markers for differentiation between renal allograft rejection and immunosuppressant toxicity in rat urine

Introduction

Although current immunosuppressive protocols have dramatically improved 1-year survival of kidney transplants, there has been less progress in terms of long-term graft survival over the last two decades. The key to avoiding late graft loss is early diagnosis and differentiation between anti-allograft immune processes and immunosuppressant toxicity (IS-Tox). Modern bioanalytical technologies have opened new opportunities for the development of sensitive and specific diagnostic tools. There is an immediate need for biomarkers that are able to differentiate between renal allograft rejection and immunosuppressant toxicity.

Objective

To test our hypothesis that changes of metabolite patterns in urine have the potential to serve as a non-invasive combinatorial biomarker that can differentiate between allograft immune reactions and IS-Tox.

Methods

We used ¹H-NMR spectroscopy and Luminex multiplexing for metabolic profiling of rat urine and the analysis of protein biomarkers in urine and plasma, respectively, to compare the effects of chronic allograft rejection in a Fisher-to-Lewis rat transplant model with IS-Tox induced by cyclosporine, tacrolimus and/or sirolimus in Lewis rats.

Results

Our results showed that, while IS-Tox caused changes in metabolite patterns that are typically associated with proximal tubule damage, rejection caused more profuse changes not specifically focused on a particular kidney region. Moreover, metabolite pattern changes were more sensitive than changes in protein markers that were evident only during the later stages of rejection.

Conclusion

The present study provides first proof-of-concept that longitudinal monitoring of urine metabolite markers has the potential to differentiate between early renal allograft rejection and immunosuppressant nephrotoxicity.

     

A framework for personalized medicine: prediction of drug sensitivity in cancer by proteomic profiling

Background

The goal of personalized medicine is to provide patients optimal drug screening and treatment based on individual genomic or proteomic profiles. Reverse-Phase Protein Array (RPPA) technology offers proteomic information of cancer patients which may be directly related to drug sensitivity. For cancer patients with different drug sensitivity, the proteomic profiling reveals important pathophysiologic information which can be used to predict chemotherapy responses.

Results

The goal of this paper is to present a framework for personalized medicine using both RPPA and drug sensitivity (drug resistance or intolerance). In the proposed personalized medicine system, the prediction of drug sensitivity is obtained by a proposed augmented naive Bayesian classifier (ANBC) whose edges between attributes are augmented in the network structure of naive Bayesian classifier. For discriminative structure learning of ANBC, local classification rate (LCR) is used to score augmented edges, and greedy search algorithm is used to find the discriminative structure that maximizes classification rate (CR). Once a classifier is trained by RPPA and drug sensitivity using cancer patient samples, the classifier is able to predict the drug sensitivity given RPPA information from a patient.

Conclusion

In this paper we proposed a framework for personalized medicine where a patient is profiled by RPPA and drug sensitivity is predicted by ANBC and LCR. Experimental results with lung cancer data demonstrate that RPPA can be used to profile patients for drug sensitivity prediction by Bayesian network classifier, and the proposed ANBC for personalized cancer medicine achieves better prediction accuracy than naive Bayes classifier in small sample size data on average and outperforms other the state-of-the-art classifier methods in terms of classification accuracy.

     

Identification of common oncogenic and early developmental pathways in the ovarian carcinomas controlling by distinct prognostically significant microRNA subsets

Background

High-grade serous ovarian carcinoma (HG-SOC) is the dominant tumor histologic type in epithelial ovarian cancers, exhibiting highly aberrant microRNA expression profiles and diverse pathways that collectively determine the disease aggressiveness and clinical outcomes. However, the functional relationships between microRNAs, the common pathways controlled by the microRNAs and their prognostic and therapeutic significance remain poorly understood.

Methods

We investigated the gene expression patterns of microRNAs in the tumors of 582 HG-SOC patients to identify prognosis signatures and pathways controlled by tumor miRNAs. We developed a variable selection and prognostic method, which performs a robust selection of small-sized subsets of the predictive features (e.g., expressed microRNAs) that collectively serves as the biomarkers of cancer risk and progression stratification system, interconnecting these features with common cancer-related pathways.

Results

Across different cohorts, our meta-analysis revealed two robust and unbiased miRNA-based prognostic classifiers. Each classifier reproducibly discriminates HG-SOC patients into high-confidence low-, intermediate- or high-prognostic risk subgroups with essentially different 5-year overall survival rates of 51.6-85%, 20-38.1%, and 0-10%, respectively. Significant correlations of the risk subgroup’s stratification with chemotherapy treatment response were observed. We predicted specific target genes involved in nine cancer-related and two oocyte maturation pathways (neurotrophin and progesterone-mediated oocyte maturation), where each gene can be controlled by more than one miRNA species of the distinct miRNA HG-SOC prognostic classifiers.

Conclusions

We identified robust and reproducible miRNA-based prognostic subsets of the of HG-SOC classifiers. The miRNAs of these classifiers could control nine oncogenic and two developmental pathways, highlighting common underlying pathologic mechanisms and perspective targets for the further development of a personalized prognosis assay(s) and the development of miRNA-interconnected pathway-centric and multi-agent therapeutic intervention.

     

Classifying mild traumatic brain injuries with functional network analysis

Background

Traumatic brain injury (TBI) represents a critical health problem of which timely diagnosis and treatment remain challenging. TBI is a result of an external force damaging brain tissue, accompanied by delayed pathogenic events which aggravate the injury. Molecular responses to different mild TBI subtypes have not been well characterized. TBI subtype classification is an important step towards the development and application of novel treatments. The computational systems biology approach is proved to be a promising tool in biomarker discovery for central nervous system injury.

Results

In this study, we have performed a network-based analysis on gene expression profiles to identify functional gene subnetworks. The gene expression profiles were obtained from two experimental models of injury in rats: the controlled cortical impact and the fluid percussion injury. Our method integrates protein interaction information with gene expression profiles to identify subnetworks of genes as biomarkers. We have demonstrated that the selected gene subnetworks are more accurate to classify the heterogeneous responses to different injury models, compared to conventional analysis using individual marker genes selected without network information.

Conclusions

The systems approach can lead to a better understanding of the underlying complexities of the molecular responses after TBI and the identified subnetworks will have important prognostic functions for patients who sustain mild TBIs.

     

Characterizing biomarkers in osteosarcoma metastasis based on an ego-network

Objectives

To characterize biomarkers that underlie osteosarcoma (OS) metastasis based on an ego-network.

Results

From the microarray data, we obtained 13,326 genes. By combining PPI data and microarray data, 10,520 shared genes were found and constructed into ego-networks. 17 significant ego-networks were identified with p < 0.05. In the pathway enrichment analysis, seven ego-networks were identified with the most significant pathway.

Conclusions

These significant ego-modules were potential biomarkers that reveal the potential mechanisms in OS metastasis, which may contribute to understanding cancer prognoses and providing new perspectives in the treatment of cancer.

     

Models of cell signaling uncover molecular mechanisms of high-risk neuroblastoma and predict disease outcome

Background

Despite the progress in neuroblastoma therapies the mortality of high-risk patients is still high (40–50%) and the molecular basis of the disease remains poorly known. Recently, a mathematical model was used to demonstrate that the network regulating stress signaling by the c-Jun N-terminal kinase pathway played a crucial role in survival of patients with neuroblastoma irrespective of their MYCN amplification status. This demonstrates the enormous potential of computational models of biological modules for the discovery of underlying molecular mechanisms of diseases.

Results

Since signaling is known to be highly relevant in cancer, we have used a computational model of the whole cell signaling network to understand the molecular determinants of bad prognostic in neuroblastoma. Our model produced a comprehensive view of the molecular mechanisms of neuroblastoma tumorigenesis and progression.

Conclusion

We have also shown how the activity of signaling circuits can be considered a reliable model-based prognostic biomarker.

Reviewers

This article was reviewed by Tim Beissbarth, Wenzhong Xiao and Joanna Polanska. For the full reviews, please go to the Reviewers’ comments section.

     

Monoacylglycerol lipase promotes progression of hepatocellular carcinoma via NF-κB-mediated epithelial-mesenchymal transition

Background

Monoacylglycerol lipase (MAGL), a critical lipolytic enzyme, has emerged as a key regulator of tumor progression, yet its biological function and clinical significance in hepatocellular carcinoma (HCC) is still unknown.

Methods

In this study, we used a tissue microarray containing samples from 170 HCC patients to evaluate the expression of MAGL and its correlation with other clinicopathologic characteristics. In addition, we investigated the regulating effects of MAGL on various HCC lines. Finally, we identified the NF-κB signaling pathway participated in MAGL-mediated epithelial-mesenchymal transition (EMT) using HCC cell lines with different metastatic potentials.

Results

The expression of MAGL was significantly higher in HCC tumors than in matched peritumor tissues. Specifically, high MAGL expression was found in tumors with larger tumor size, microvascular invasion, poor differentiation, or advanced TNM stage. In addition, the clinical prognosis for the MAGL^high group was markedly poorer than that for the MAGL^low group in the 1-, 3-, and 5-year overall survival times and recurrence rates of HCC patients. MAGL expression was an independent prognostic factor for both survival and recurrence after curative resection. Furthermore, the upregulation of MAGL in HCC cells promoted cell growth and invasiveness abilities, and accompanied by EMT. In contrast, downregulation of MAGL obviously inhibited these characteristics. Moreover, further investigations verified that MAGL facilitates HCC progression via NF-κB-mediated EMT process.

Conclusions

Our findings demonstrate MAGL could promote HCC progression by the induction of EMT and suggest a potential therapeutic target, as well as a biomarker for prognosis, in patients with HCC.

     

Metabolic characterization of hepatitis B virus-related liver cirrhosis using NMR-based serum metabolomics

Introduction

Liver cirrhosis (LC) is an advanced liver disease that can develop into hepatocellular carcinoma. Hepatitis B virus (HBV) infection is one of the main causes of LC. Therefore, there is an urgent need for developing a new method to monitor the progression of HBV-related LC (HBV-LC).

Objectives

In this study, we attempted to examine serum metabolic changes in healthy individuals as well as patients with HBV and HBV-LC. Furthermore, potential metabolite biomarkers were identified to evaluate patients progressed from health to HBV-LC.

Methods

Metabolic profiles in the serum of healthy individuals as well as patients with HBV and HBV-LC were detected using an NMR-based metabolomic approach. Univariate and multivariate analyses were conducted to analyze serum metabolic changes during HBV-LC progression. Moreover, potential metabolite biomarkers were explored by receiver operating characteristic curve analysis.

Results

Serum metabolic changes were closely associated with the progression of HBV-LC, mainly involving energy metabolism, protein metabolism, lipid metabolism and microbial metabolism. Serum histidine was identified as a potential biomarker for HBV patients. Acetate, formate, pyruvate and glutamine in the serum were identified as a potential biomarker panel for patients progressed from HBV to HBV-LC. In addition, phenylalanine, unsaturated lipid, n-acetylglycoprotein and acetone in the serum could be considered as a potential common biomarkers panel for these patients.

Conclusion

NMR-based serum metabolomic approach could be a promising tool to monitor the progression of liver disease. Different metabolites may reflect different stages of liver disease.

     

Study of the tumor microenvironment during breast cancer progression

Background

Different cells and mediators in the tumor microenvironment play important roles in the progression of breast cancer. The aim of this study was to determine the composition of the microenvironment during tumor progression in order to discover new related biomarkers and potentials for targeted therapy.

Methods

In this study, breast cancer biopsies from four different stages, and control breast biopsies were collected. Then, the mRNA expression of several markers related to different CD4⁺ T cell subsets including regulatory T cells (Treg), T helper (Th) type 1, 2 and 17 were determined. In addition, we investigated the expression of two inflammatory cytokines (TNF-α and IL-6) and inflammatory mediators including FASL, IDO, SOCS1, VEGF, and CCR7.

Results

The results showed that the expression of Th1 and Th17 genes was decreased in tumor tissues compared to control tissues. In addition, we found that the gene expression related to these two cell subsets decreased during cancer progression. Moreover, the expression level of TNF-α increased with tumor progression.

Conclusion

We conclude that the expression of genes related to immune response and inflammation is different between tumor tissues and control tissues. In addition, this difference was perpetuated through the different stages of cancer.

     

Plasma lipidomics identifies novel biomarkers in patients with hepatitis B virus-related acute-on-chronic liver failure

Introduction

Acute-on-chronic liver failure (ACLF) is a fatal syndrome that presents with acute deterioration of liver function in chronic hepatitis B virus (HBV) patients. However, reliable diagnostic and prognostic biomarkers are scarce.

Objectives

The aim of this study to identify lipid species associated with HBV infection as well as novel lipid biomarkers for HBV-ACLF.

Methods

High performance liquid chromatography–tandem mass spectrometry was used for targeted lipidomic analyses of 147 lipid species. Fasting-state plasma samples from 74 HBV-ACLF patients, 86 HBV-non-ACLF patients [30 HBV-immune tolerant (HBV-IT) and 56 chronic hepatitis B] and 20 healthy controls. Univariate and multivariate analyses examined changes in lipid species among patient groups.

Results

The HBV-ACLF and HBV-non-ACLF groups had distinctly different lipid profiles, while the HC and HBV-IT groups had similar lipid profiles. Further, lysophosphatidylcholine (LPC) 22:6, cholesterol ester (CE) 22:6, CE 20:4, CE 18:2 and CE 18:1 could be used as potential biomarkers for the early prediction of ACLF. Meanwhile, univariate and multivariate analyses identified CE 20:4, LPC 16:0, LPC 18:0, phosphatidylcholine (PC) 40:6 and PC 32:0 as putative diagnostic biomarkers of HBV-ACLF. Moreover, LPC 16:0 and LPC 18:0 were significantly associated with model for end stage liver disease (MELD) scores, and the two lipid species combined with MELD score had significant capability to predict the 6-month mortality.

Conclusions

Our study revealed that lipid metabolism disorders were significantly associated with the severity of liver inflammatory injury rather than HBV infection in patients with chronic HBV infection, and specific lipid species could be used as potentially biomarkers for diagnosis and prognosis in HBV-ACLF.

     

Detection of potential new biomarkers of atherosclerosis by probe electrospray ionization mass spectrometry

Introduction

Atherosclerotic diseases are the leading cause of death worldwide. Biomarkers of atherosclerosis are required to monitor and prevent disease progression. While mass spectrometry is a promising technique to search for such biomarkers, its clinical application is hampered by the laborious processes for sample preparation and analysis.

Methods

We developed a rapid method to detect plasma metabolites by probe electrospray ionization mass spectrometry (PESI-MS), which employs an ambient ionization technique enabling atmospheric pressure rapid mass spectrometry. To create an automatic diagnosis system of atherosclerotic disorders, we applied machine learning techniques to the obtained spectra.

Results

Using our system, we successfully discriminated between rabbits with and without dyslipidemia. The causes of dyslipidemia (genetic lipoprotein receptor deficiency or dietary cholesterol overload) were also distinguishable by this method. Furthermore, after induction of atherosclerosis in rabbits with a cholesterol-rich diet, we were able to detect dynamic changes in plasma metabolites. The major metabolites detected by PESI-MS included cholesterol sulfate and a phospholipid (PE18:0/20:4), which are promising new biomarkers of atherosclerosis.

Conclusion

We developed a remarkably fast and easy method to detect potential new biomarkers of atherosclerosis in plasma using PESI-MS.

     

A review of metabolism-associated biomarkers in lung cancer diagnosis and treatment

Introduction

Lung cancer continues to be the leading cause of cancer-related mortality worldwide. Early detection has proven essential to extend survival. Genomic and proteomic advances have provided impetus to the effort dedicated to detect and diagnose the disease at an earlier stage. Recently, the study of metabolites associated with tumor formation and progression has inaugurated the era of cancer metabolomics to aid in this effort.

Objectives

This review summarizes recent work regarding novel metabolites with the potential to serve as biomarkers for early lung tumor detection, evaluation of disease progression, and prediction of patient outcomes.

Method

We compare the metabolite profiling of cancer patients with that of healthy individuals, and the metabolites identified in tissue and biofluid samples and their usefulness as lung cancer biomarkers. We discuss metabolite alterations in tumor versus paired non-tumor lung tissues, as well as metabolite alterations in different stages of lung cancers and their usefulness as indicators of disease progression and overall survival. We evaluate metabolite dysregulation in different types of lung cancers, and those associated with lung cancer versus other lung diseases. We also examine metabolite differences between lung cancer patients and smokers/risk-factor individuals.

Result

Although an extensive list of metabolites has been evaluated to distinguish between these cases, refinement of methods is further required for adequate patient diagnosis and treatment.

Conclusion

We conclude that with technological advancement, metabolomics may be able to replace more invasive and costly diagnostic procedures while also providing the means to more effectively tailor treatment to patient-specific tumors.