Cytoplasmic Skp2 expression is increased in human melanoma and correlated with patient survival

Background

S-phase kinase protein 2 (Skp2), an F-box protein, targets cell cycle regulators via ubiquitin-mediated degradation. Skp2 is frequently overexpressed in a variety of cancers and associated with patient survival. In melanoma, however, the prognostic significance of subcellular Skp2 expression remains controversial.

Methods

To investigate the role of Skp2 in melanoma development, we constructed tissue microarrays and examined Skp2 expression in melanocytic lesions at different stages, including 30 normal nevi, 61 dysplastic nevi, 290 primary melanomas and 146 metastatic melanomas. The TMA was assessed for cytoplasmic and nuclear Skp2 expression by immunohistochemistry. The Kaplan-Meier method was used to evaluate the patient survival. The univariate and multivariate Cox regression models were performed to estimate the harzard ratios (HR) at five-year follow-up.

Results

Cytoplasmic but not nuclear Skp2 expression was gradually increased from normal nevi, dysplastic nevi, primary melanomas to metastatic melanomas. Cytoplasmic Skp2 expression correlated with AJCC stages (I vs II–IV, P<0.001), tumor thickness (≤2.00 vs >2.00 mm, P<0.001) and ulceration (P = 0.005). Increased cytoplasmic Skp2 expression was associated with a poor five-year disease-specific survival of patients with primary melanoma (P = 0.018) but not metastatic melanoma (P>0.05).

Conclusion

This study demonstrates that cytoplasmic Skp2 plays an important role in melanoma pathogenesis and its expression correlates with patient survival. Our data indicate that cytoplasmic Skp2 may serve as a potential biomarker for melanoma progression and a therapeutic target for this disease.     

DACH1: Its Role as a Classifier of Long Term Good Prognosis in Luminal Breast Cancer

Background

Oestrogen receptor (ER) positive (luminal) tumours account for the largest proportion of females with breast cancer. Theirs is a heterogeneous disease presenting clinical challenges in managing their treatment. Three main biological luminal groups have been identified but clinically these can be distilled into two prognostic groups in which Luminal A are accorded good prognosis and Luminal B correlate with poor prognosis. Further biomarkers are needed to attain classification consensus. Machine learning approaches like Artificial Neural Networks (ANNs) have been used for classification and identification of biomarkers in breast cancer using high throughput data. In this study, we have used an artificial neural network (ANN) approach to identify DACH1 as a candidate luminal marker and its role in predicting clinical outcome in breast cancer is assessed.

Materials and methods

A reiterative ANN approach incorporating a network inferencing algorithm was used to identify ER-associated biomarkers in a publically available cDNA microarray dataset. DACH1 was identified in having a strong influence on ER associated markers and a positive association with ER. Its clinical relevance in predicting breast cancer specific survival was investigated by statistically assessing protein expression levels after immunohistochemistry in a series of unselected breast cancers, formatted as a tissue microarray.

Results

Strong nuclear DACH1 staining is more prevalent in tubular and lobular breast cancer. Its expression correlated with ER-alpha positive tumours expressing PgR, epithelial cytokeratins (CK)18/19 and ‘luminal-like’ markers of good prognosis including FOXA1 and RERG (p<0.05). DACH1 is increased in patients showing longer cancer specific survival and disease free interval and reduced metastasis formation (p<0.001). Nuclear DACH1 showed a negative association with markers of aggressive growth and poor prognosis.

Conclusion

Nuclear DACH1 expression appears to be a Luminal A biomarker predictive of good prognosis, but is not independent of clinical stage, tumour size, NPI status or systemic therapy.     

C-Terminal Tensin-Like Protein Is a Novel Prognostic Marker for Primary Melanoma Patients

Background

C-terminal tensin-like protein (Cten) is a focal adhesion protein originally identified as a tumor suppressor in prostate cancer. It has since been found to be overexpressed and function as an oncogene in numerous other cancers, but the expression status of Cten in melanoma is still unknown.

Methods

Using tissue microarrays containing 562 melanocytic lesions, we evaluated Cten protein expression by immunohistochemistry. The association between Cten expression and patient survival was examined using Kaplan-Meier survival analysis, and univariate and multivariate Cox regression analyses were used to estimate the crude and adjusted hazard ratios.

Results

Strong Cten expression was detected in 7%, 24%, 41%, and 46% of normal nevi, dysplastic nevi, primary melanoma, and metastatic melanoma samples, respectively, and Cten expression was found to be significantly higher in dysplastic nevi compared to normal nevi (P = 0.046), and in primary melanoma compared to dysplastic nevi (P = 0.003), but no difference was observed between metastatic and primary melanoma. Cten staining also correlated with AJCC stages (P = 0.015) and primary tumor thickness (P = 0.002), with Cten expression being induced in the transition from thin (<1mm) to thick (≥1mm) melanomas. Strong Cten expression was significantly associated with a worse 5-year overall (P = 0.008) and disease-specific survival (P = 0.004) for primary melanoma patients, and multivariate Cox regression analysis revealed that Cten expression was an independent prognostic marker for these patients (P = 0.038 for overall survival; P = 0.021 for disease-specific survival).

Conclusion

Our findings indicate that induction of Cten protein expression is a relatively early event in melanoma progression, and that Cten has the potential to serve as a prognostic marker for primary melanoma patients.     

Differential volatile signatures from skin, naevi and melanoma: a novel approach to detect a pathological process

Background

Early detection of melanoma is of great importance to reduce mortality. Discovering new melanoma biomarkers would improve early detection and diagnosis. Here, we present a novel approach to detect volatile compounds from skin.

Methods and Findings

We used Head Space Solid Phase Micro-Extraction (HS-SPME) and gas chromatography/mass spectrometry (GC/MS) to identify volatile signatures from melanoma, naevi and skin samples. We hypothesized that the metabolic state of tissue alters the profile of volatile compounds. Volatiles released from fresh biopsy tissue of melanoma and benign naevus were compared based on their difference in frequency distribution and their expression level. We also analyzed volatile profiles from frozen tissue, including skin and melanoma.

Conclusions

Three volatiles, 4-methyl decane, dodecane and undecane were preferentially expressed in both fresh and frozen melanoma, indicating that they are candidate biomarkers. Twelve candidate biomarkers evaluated by fuzzy logic analysis of frozen samples distinguished melanoma from skin with 89% sensitivity and 90% specificity. Our results demonstrate proof-of-principle that there is differential expression of volatiles in melanoma. Our volatile metabolomic approach will lead to a better understanding of melanoma and can enable development of new diagnostic and treatment strategies based on altered metabolism.     

Down-regulation of the inhibitor of growth family member 4 (ING4) in different forms of pulmonary fibrosis

Background

Recent evidence has underscored the role of hypoxia and angiogenesis in the pathogenesis of idiopathic fibrotic lung disease. Inhibitor of growth family member 4 (ING4) has recently attracted much attention as a tumor suppressor gene, due to its ability to inhibit cancer cell proliferation, migration and angiogenesis. The aim of our study was to investigate the role of ING4 in the pathogenesis of pulmonary fibrosis both in the bleomycin (BLM)-model and in two different types of human pulmonary fibrosis, including idiopathic pulmonary fibrosis (IPF) and cryptogenic organizing pneumonia (COP).

Methods

Experimental model of pulmonary fibrosis was induced by a single tail vein injection of bleomycin in 6- to 8-wk-old C57BL/6mice. Tissue microarrays coupled with qRT-PCR and immunohistochemistry were applied in whole lung samples and paraffin-embedded tissue sections of 30 patients with IPF, 20 with COP and 20 control subjects.

Results

A gradual decline of ING4 expression in both mRNA and protein levels was reported in the BLM-model. ING4 was also found down-regulated in IPF patients compared to COP and control subjects. Immunolocalization analyses revealed increased expression in areas of normal epithelium and in alveolar epithelium surrounding Masson bodies, in COP lung, whereas showed no expression within areas of active fibrosis within IPF and COP lung. In addition, ING4 expression levels were negatively correlated with pulmonary function parameters in IPF patients.

Conclusion

Our data suggest a potential role for ING4 in lung fibrogenesis. ING4 down-regulation may facilitate aberrant vascular remodelling and fibroblast proliferation and migration leading to progressive disease.     

Proteasomal Degradation of Mcl-1 by Maritoclax Induces Apoptosis and Enhances the Efficacy of ABT-737 in Melanoma Cells

Background and purpose

Metastatic melanoma remains one of the most invasive and highly drug resistant cancers. The over expression of anti-apoptotic protein Mcl-1 has been associated with inferior survival, poor prognosis and chemoresistance of malignant melanoma. A BH3 mimetic, ABT-737, has demonstrated efficacy in several forms of cancers. However, the efficacy of ABT-737 depends on Mcl-1. Because the over expression of Mcl-1 is frequently observed in melanoma, specifically targeting of Mcl-1 may overcome the resistance of ABT-737. In this study, we investigated the effects of Maritoclax, a novel Mcl-1-selective inhibitor, alone and in combination with ABT-737, on the survival of human melanoma cells.

Experimental approach

For cell viability assessment we performed MTT assay. Apoptosis was determined using western blot and flow cytometric analysis.

Key results

The treatment of Maritoclax reduced the cell viability of melanoma cells with an IC₅₀ of between 2.2–5.0 µM. Further, treatment of melanoma cells with Maritoclax showed significant decrease in Mcl-1 expression. We found that Maritoclax was able to induce apoptosis in melanoma cells in a caspase-dependent manner. Moreover, Maritoclax induced Mcl-1 degradation via the proteasome system, which was associated with its pro-apoptotic activity. We also found that Maritoclax treatment increased mitochondrial translocation of Bim and Bmf. Importantly, Maritoclax markedly enhanced the efficacy of ABT-737 against melanoma cells in both two- and three-dimensional spheroids.

Conclusions and implications

Taken together, these results suggest that targeting of Mcl-1 by Maritoclax may represent a new therapeutic strategy for melanoma treatment that warrants further investigation as a single therapy or in combination with other agents such as Bcl-2 inhibitors.     

Prospective Study of the Evolution of Blood Lymphoid Immune Parameters during Dacarbazine Chemotherapy in Metastatic and Locally Advanced Melanoma Patients

Background

The importance of immune responses in the control of melanoma growth is well known. However, the implication of these antitumor immune responses in the efficacy of dacarbazine, a cytotoxic drug classically used in the treatment of melanoma, remains poorly understood in humans.

Methods

In this prospective observational study, we performed an immunomonitoring of eleven metastatic or locally advanced patients treated with dacarbazine as a first line of treatment. We assessed by flow cytometry lymphoid populations and their activation state; we also isolated NK cells to perform in vitro cytotoxicity tests.

Results

We found that chemotherapy induces lymphopenia and that a significantly higher numbers of naïve CD4⁺ T cells and lower proportion of Treg before chemotherapy are associated with disease control after dacarbazine treatment. Interestingly, NK cell cytotoxicity against dacarbazine-pretreated melanoma cells is only observed in NK cells from patients who achieved disease control.

Conclusion

Together, our data pinpoint that some immune factors could help to predict the response of melanoma patients to dacarbazine. Future larger scale studies are warranted to test their validity as prediction markers.     

Do Non-Glycaemic Markers Add Value to Plasma Glucose and Hemoglobin A1c in Predicting Diabetes? Yuport Health Checkup Center Study

Background

Many markers have been indicated as predictors of type 2 diabetes. However, the question of whether or not non-glycaemic (blood) biomarkers and non-blood biomarkers have a predictive additive utility when combined with glycaemic (blood) biomarkers is unknown. The study aim is to assess this additive utility in a large Japanese population.

Methods

We used data from a retrospective cohort study conducted from 1998 to 2002 for the baseline and 2002 to 2006 for follow-up, inclusive of 5,142 men (mean age of 51.9 years) and 4,847 women (54.1 years) at baseline. The cumulative incidence of diabetes [defined either as a fasting plasma glucose (FPG) ≥7.00 mmol/l or as clinically diagnosed diabetes] was measured. In addition to glycaemic biomarkers [FPG and hemoglobin A1c (HbA1c)], we examined the clinical usefulness of adding non-glycaemic biomarkers and non-blood biomarkers, using sensitivity and specificity, and the area under the curve (AUC) of the receiver operating characteristics.

Results

The AUCs to predict diabetes were 0.874 and 0.924 for FPG, 0.793 and 0.822 for HbA1c, in men and women, respectively. Glycaemic biomarkers were the best and second-best for diabetes prediction among the markers. All non-glycaemic markers (except uric acid in men and creatinine in both sexes) predicted diabetes. Among these biomarkers, the highest AUC in the single-marker analysis was 0.656 for alanine aminotransferase (ALT) in men and 0.740 for body mass index in women. The AUC of the combined markers of FPG and HbA1c was 0.895 in men and 0.938 in women, which were marginally increased to 0.904 and 0.940 when adding ALT, respectively.

Conclusions

AUC increments were marginal when adding non-glycaemic biomarkers and non-blood biomarkers to the classic model based on FPG and HbA1c. For the prediction of diabetes, FPG and HbA1c are sufficient and the other markers may not be needed in clinical practice.     

Primary mucosal sinonasal melanoma—Case report and review of the literature. The role of complex treatment-surgery and adjuvant radiotherapy

Aim

The place of adjuvant radiotherapy in the treatment of sinonasal melanoma.

Background

Sinonasal mucosal melanoma is a rare disease with poor prognosis and requires a complex treatment. Elective neck dissection in patients with N0 and adjuvant radiotherapy has been a source of controversy. High late regional recurrence rates rise questions about elective irradiation of the neck nodes in patients with N0 stage disease.

Methods

We present our two years’ follow up in a case of locally advanced sinonasal melanoma and literature review of the treatment options for mucosal melanoma.

Results

In locally advanced sinonasal melanoma treated with surgical resection, postoperative radiotherapy and chemotherapy we had local tumor control. Two years later, a regional contralateral recurrence without distant metastasis occurred.

Conclusions

Literature data for frequent neck lymph nodes recurrences justify elective neck dissection. Postoperative elective neck radiotherapy for patients with locally advanced sinonasal melanoma and clinically N0 appears to decrease the rate of late regional recurrences.     

Discovery of Novel Serum Biomarkers for Prenatal Down Syndrome Screening by Integrative Data Mining

Background

To facilitate the experimental search for novel maternal serum biomarkers in prenatal Down Syndrome screening, we aimed to create a set of candidate biomarkers using a data mining approach.

Methodology/Principal Findings

Because current screening markers are derived from either fetal liver or placental trophoblasts, we reasoned that new biomarkers can primarily be found to be derived from these two tissues. By applying a three-stage filtering strategy on publicly available data from different sources, we identified 49 potential blood-detectable protein biomarkers. Our set contains three biomarkers that are currently widely used in either first- or second-trimester screening (AFP, PAPP-A and fβ-hCG), as well as ten other proteins that are or have been examined as prenatal serum markers. This supports the effectiveness of our strategy and indicates the set contains other markers potentially applicable for screening.

Conclusions/Significance

We anticipate the set will help support further experimental studies for the identification of new Down Syndrome screening markers in maternal blood.     

Development of a Novel Heart Failure Risk Tool: The Barcelona Bio-Heart Failure Risk Calculator (BCN Bio-HF Calculator)

Background

A combination of clinical and routine laboratory data with biomarkers reflecting different pathophysiological pathways may help to refine risk stratification in heart failure (HF). A novel calculator (BCN Bio-HF calculator) incorporating N-terminal pro B-type natriuretic peptide (NT-proBNP, a marker of myocardial stretch), high-sensitivity cardiac troponin T (hs-cTnT, a marker of myocyte injury), and high-sensitivity soluble ST2 (ST2), (reflective of myocardial fibrosis and remodeling) was developed.

Methods

Model performance was evaluated using discrimination, calibration, and reclassification tools for 1-, 2-, and 3-year mortality. Ten-fold cross-validation with 1000 bootstrapping was used.

Results

The BCN Bio-HF calculator was derived from 864 consecutive outpatients (72% men) with mean age 68.2±12 years (73%/27% New York Heart Association (NYHA) class I-II/III-IV, LVEF 36%, ischemic etiology 52.2%) and followed for a median of 3.4 years (305 deaths). After an initial evaluation of 23 variables, eight independent models were developed. The variables included in these models were age, sex, NYHA functional class, left ventricular ejection fraction, serum sodium, estimated glomerular filtration rate, hemoglobin, loop diuretic dose, β-blocker, Angiotensin converting enzyme inhibitor/Angiotensin-2 receptor blocker and statin treatments, and hs-cTnT, ST2, and NT-proBNP levels. The calculator may run with the availability of none, one, two, or the three biomarkers. The calculated risk of death was significantly changed by additive biomarker data. The average C-statistic in cross-validation analysis was 0.79.

Conclusions

A new HF risk-calculator that incorporates available biomarkers reflecting different pathophysiological pathways better allowed individual prediction of death at 1, 2, and 3 years.     

Soluble Biomarkers of Cartilage and Bone Metabolism in Early Proof of Concept Trials in Psoriatic Arthritis: Effects of Adalimumab Versus Placebo

Background

There is growing interest in soluble biomarkers that could be used on the group level for screening purposes in small proof of principle studies during early drug development. We investigated early changes in serum levels of several candidate biomarkers involved in cartilage and bone metabolism following the initiation of adalimumab as a prototypic active treatment in psoriatic arthritis (PsA) compared to placebo.

Materials and Methods

Twenty-four PsA patients were randomized to receive either adalimumab 40 mg s.c. every other week or placebo for 4 weeks, followed by an open label extension phase. Serum samples were obtained at baseline and after 4 and 12 weeks of treatment and analyzed for levels of CPII and PINP (synthesis of type II and type I procollagen), melanoma inhibitory activity (MIA) (chondrocyte anabolism), matrix metalloproteinase (MMP)-3, C2C and cartilage oligomeric matrix protein (COMP) (type II collagen degradation), osteocalcin (OC) (bone formation), NTX-I and ICTP (both type I collagen degradation).

Results

After 4 weeks, there was a significant decrease in serum MMP-3 levels in adalimumab-treated patients (P<0.005), while no change was observed in the placebo group. A significant increase in serum MIA was noted after adalimumab therapy (P<0.005) but not after placebo treatment. After 12 weeks, there was a marked reduction in serum MMP-3 in both groups (P<0.005), whereas other markers did not show significant changes compared to baseline.

Conclusion

MMP-3 and MIA could serve as soluble biomarkers associated with inflammation as well as joint remodelling and destruction and may, together with clinical evaluation and in combination with other biomarkers, assist in distinguishing between effective and ineffective therapy in small, proof-of-principle studies of short duration in PsA.

Trial Registration

Current Controlled Trials ISRCTN23328456     

Effect of size and heterogeneity of samples on biomarker discovery: synthetic and real data assessment

Motivation

The identification of robust lists of molecular biomarkers related to a disease is a fundamental step for early diagnosis and treatment. However, methodologies for the discovery of biomarkers using microarray data often provide results with limited overlap. These differences are imputable to 1) dataset size (few subjects with respect to the number of features); 2) heterogeneity of the disease; 3) heterogeneity of experimental protocols and computational pipelines employed in the analysis. In this paper, we focus on the first two issues and assess, both on simulated (through an in silico regulation network model) and real clinical datasets, the consistency of candidate biomarkers provided by a number of different methods.

Methods

We extensively simulated the effect of heterogeneity characteristic of complex diseases on different sets of microarray data. Heterogeneity was reproduced by simulating both intrinsic variability of the population and the alteration of regulatory mechanisms. Population variability was simulated by modeling evolution of a pool of subjects; then, a subset of them underwent alterations in regulatory mechanisms so as to mimic the disease state.

Results

The simulated data allowed us to outline advantages and drawbacks of different methods across multiple studies and varying number of samples and to evaluate precision of feature selection on a benchmark with known biomarkers. Although comparable classification accuracy was reached by different methods, the use of external cross-validation loops is helpful in finding features with a higher degree of precision and stability. Application to real data confirmed these results.     

Measurement of Phospholipids May Improve Diagnostic Accuracy in Ovarian Cancer

Background

More than two-thirds of women who undergo surgery for suspected ovarian neoplasm do not have cancer. Our previous results suggest phospholipids as potential biomarkers of ovarian cancer. In this study, we measured the serum levels of multiple phospholipids among women undergoing surgery for suspected ovarian cancer to identify biomarkers that better predict whether an ovarian mass is malignant.

Methodology/Principal Findings

We obtained serum samples preoperatively from women with suspected ovarian cancer enrolled through a prospective, population-based rapid ascertainment system. Samples were analyzed from all women in whom a diagnosis of epithelial ovarian cancer (EOC) was confirmed and from benign disease cases randomly selected from the remaining (non-EOC) samples. We measured biologically relevant phospholipids using liquid chromatography/electrospray ionization mass spectrometry. We applied a powerful statistical and machine learning approach, Hybrid huberized support vector machine (HH-SVM) to prioritize phospholipids to enter the biomarker models, and used cross-validation to obtain conservative estimates of classification error rates.

Results

The HH-SVM model using the measurements of specific combinations of phospholipids supplements clinical CA125 measurement and improves diagnostic accuracy. Specifically, the measurement of phospholipids improved sensitivity (identification of cases with preoperative CA125 levels below 35) among two types of cases in which CA125 performance is historically poor - early stage cases and those of mucinous histology. Measurement of phospholipids improved the identification of early stage cases from 65% (based on CA125) to 82%, and mucinous cases from 44% to 88%.

Conclusions/Significance

Levels of specific serum phospholipids differ between women with ovarian cancer and those with benign conditions. If validated by independent studies in the future, these biomarkers may serve as an adjunct at the time of clinical presentation, to distinguish between women with ovarian cancer and those with benign conditions with shared symptoms and features.     

Inflammatory and repair serum biomarker pattern. Association to clinical outcomes in COPD

Background

The relationship between serum biomarkers and clinical expressions of COPD is limited. We planned to further describe this association using markers of inflammation and injury and repair.

Methods

We studied lung function, comorbidities, exercise tolerance, BODE index, and quality of life in 253 COPD patients and recorded mortality over three years. Serum levels of Interleukins 6,8 and16, tumor necrosis factor alpha (TNF α) [inflammatory panel], vascular endothelial growth factor (VEGF), and matrix metalloproteinase 9 (MMP-9) [injury and repair panel] and pulmonary and activation-regulated chemokine (PARC/CCL-18) and monocyte chemotactic protein 1 (MCP-1/CCL2) [chemoattractant panel] were measured. We related the pattern of the biomarker levels to minimal clinically important differences (MCID) using a novel visualization method [ObServed Clinical Association Results (OSCAR) plot].

Results

Levels of the inflammatory markers IL-6, TNF α were higher and those of injury and repair lower (p < 0.01) with more advanced disease (GOLD 1 vs. 4). Using the OSCAR plot, we found that patients in the highest quartile of inflammatory and lowest quartile of injury and repair biomarkers level were more clinically compromised and had higher mortality (p < 0.05).

Conclusions

In COPD, serum biomarkers of inflammation and repair are distinctly associated with important clinical parameters and survival.     

ING1 and 5-Azacytidine Act Synergistically to Block Breast Cancer Cell Growth

Background

Inhibitor of Growth (ING) proteins are epigenetic “readers” that recognize trimethylated lysine 4 of histone H3 (H3K4Me3) and target histone acetyl transferase (HAT) and histone deacetylase (HDAC) complexes to chromatin.

Methods and Principal Findings

Here we asked whether dysregulating two epigenetic pathways with chemical inhibitors showed synergistic effects on breast cancer cell line killing. We also tested whether ING1 could synergize better with chemotherapeutics that target the same epigenetic mechanism such as the HDAC inhibitor LBH589 (Panobinostat) or a different epigenetic mechanism such as 5-azacytidine (5azaC), which inhibits DNA methyl transferases. Simultaneous treatment of breast cancer cell lines with LBH589 and 5azaC did not show significant synergy in killing cells. However, combination treatment of ING1 with either LBH589 or 5azaC did show synergy. The combination of ING1b with 5azaC, which targets two distinct epigenetic mechanisms, was more effective at lower doses and enhanced apoptosis as determined by Annexin V staining and cleavage of caspase 3 and poly-ADP-ribose polymerase (PARP). ING1b plus 5azaC also acted synergistically to increase γH2AX staining indicating significant levels of DNA damage were induced. Adenoviral delivery of ING1b with 5azaC also inhibited cancer cell growth in a murine xenograft model and led to tumor regression when viral concentration was optimized in vivo.

Conclusions

These data show that targeting distinct epigenetic pathways can be more effective in blocking cancer cell line growth than targeting the same pathway with multiple agents, and that using viral delivery of epigenetic regulators can be more effective in synergizing with a chemical agent than using two chemotherapeutic agents. This study also indicates that the ING1 epigenetic regulator may have additional activities in the cell when expressed at high levels.     

Sphingolipids as New Biomarkers for Assessment of Delayed-Type Hypersensitivity and Response to Triptolide

Background

Hypersensitivity diseases are associated with many severe human illnesses, including leprosy and tuberculosis. Emerging evidence suggests that the pathogenesis and pathological mechanisms of treating these diseases may be attributable to sphingolipid metabolism.

Methods

High performance liquid chromatography-tandem mass spectrometry was employed to target and measure 43 core sphingolipids in the plasma, kidneys, livers and spleens of BALB/c mice from four experimental groups: control, delayed-type hypersensitivity (DTH) model, DTH+triptolide, and control+triptolide. Orthogonal partial least squares discriminant analysis (OPLS-DA) was used to identify potential biomarkers associated with variance between groups. Relationships between the identified biomarkers and disease markers were evaluated by Spearman correlation.

Results

As a treatment to hypersensitivity disease, triptolide significantly inhibit the ear swelling and recover the reduction of splenic index caused by DTH. The sphingolipidomic result revealed marked alterations in sphingolipid levels between groups that were associated with the effects of the disease and triptolide treatment. Based on this data, 23 potential biomarkers were identified by OPLS-DA, and seven of these biomarkers correlated markedly with the disease markers (p<0.05) by Spearman correlation.

Conclusions

These data indicate that differences in sphingolipid levels in plasma and tissues are related to DTH and treatment with triptolide. Restoration of proper sphingolipid levels may attribute to the therapeutic effect of triptolide treatment. Furthermore, these findings demonstrate that targeted sphingolipidomic analysis followed by multivariate analysis presents a novel strategy for the identification of biomarkers in biological samples.