Biomarkers for pancreatic cancer: Recent achievements in proteomics and genomics through classical and multivariate statistical methods

Identification of telomere-related lncRNAs and immunological analysis in ovarian cancer

Background

Ovarian cancer (OC) is a global malignancy characterized by metastatic invasiveness and recurrence. Long non-coding RNAs (lncRNAs) and Telomeres are closely connected with several cancers, but their potential as practical prognostic markers in OC is less well-defined.

Methods

Relevant mRNA and clinical data for OC were sourced from The Cancer Genome Atlas (TCGA) database. The telomere-related lncRNAs (TRLs) prognostic model was established by univariate/LASSO/multivariate regression analyses. The effectiveness of the TRLs model was evaluated and measured via the nomogram. Additionally, immune infiltration, tumor mutational load (TMB), and drug sensitivity were evaluated. We validated the expression levels of prognostic genes. Subsequently, PTPRD-AS1 knockdown was utilized to perform the CCK8 assay, colony formation assay, transwell assay, and wound healing assay of CAOV3 cells.

Results

A six-TRLs prognostic model (PTPRD-AS1, SPAG5-AS1, CHRM3-AS2, AC074286.1, FAM27E3, and AC018647.3) was established, which can effectively predict patient survival rates and was successfully validated using external datasets. According to the nomogram, the model could effectively predict prognosis. Furthermore, we detected the levels of regulatory T cells and M2 macrophages were comparatively higher in the high-risk TRLs group, but the levels of activated CD8 T cells and monocytes were the opposite. Finally, the low-risk group was more sensitive to anti-cancer drugs. The mRNA levels of PTPRD-AS1, SPAG5-AS1, FAM27E3, and AC018647.3 were significantly over-expressed in OC cell lines (SKOV3, A2780, CAOV3) in comparison to normal IOSE-80 cells. AC074286.1 were over-expressed in A2780 and CAOV3 cells and CHRM3-AS2 only in A2780 cells. PTPRD-AS1 knockdown decreased the proliferation, cloning, and migration of CAOV3 cells.

Conclusion

Our study identified potential biomarkers for the six-TRLs model related to the prognosis of OC.

Associations between genetically predicted levels of blood metabolites and pancreatic cancer risk

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive solid malignancies, which is featured by systematic metabolism. Thus, a better understanding of metabolic dysregulation in PDAC is important to better characterize its etiology. Here, we performed a large metabolome-wide association study (MWAS) to systematically explore associations between genetically predicted metabolite levels in blood and PDAC risk. Using data from 881 subjects of European descent in the TwinsUK Project, comprehensive genetic models were built to predict serum metabolite levels. These prediction models were applied to the genetic data of 8275 cases and 6723 controls included in the PanScan (I, II and III) and PanC4 consortia. After assessing the metabolite-PDAC risk associations by a slightly modified TWAS/FUSION framework, we identified five metabolites (including two dipeptides) showing significant associations with PDAC risk at false discovery rate (FDR) <0.05. Integrated with gut microbial information, two-sample Mendelian randomization (MR) analyses were further performed to investigate the relationship among serum metabolites, gut microbiome features and PDAC. The flavonoid-degrading bacteria Flavonifractor sp90199495 was found to be associated with metabolite X-21849 and it was also shown to be associated with PDAC risk. Collectively, our study identified novel candidate metabolites for PDAC risk, which could lead to new insights into the etiology of PDAC and improved treatment options.

Biomarkers: Promising and valuable tools towards diagnosis, prognosis and treatment of Covid-19 and other diseases

The use of biomarkers as early warning systems in the evaluation of disease risk has increased markedly in the last decade. Biomarkers are indicators of typical biological processes, pathogenic processes, or pharmacological reactions to therapy. The application and identification of biomarkers in the medical and clinical fields have an enormous impact on society. In this review, we discuss the history, various definitions, classifications, characteristics, and discovery of biomarkers. Furthermore, the potential application of biomarkers in the diagnosis, prognosis, and treatment of various diseases over the last decade are reviewed. The present review aims to inspire readers to explore new avenues in biomarker research and development.

Influence of thermal processing on the quality of hawthorn: quality markers of heat-processed hawthorn

Hawthorn and its derived products are used worldwide as foods as well as complementary medicine. During the preparation of hawthorn, heating and thermal processing are frequently reported. The thermal processing will change the medicinal purposes and modify the efficacy of hawthorn. However, details including the chemical profile shifting and quality markers of heat-processed hawthorn have not been well understood. In the paper, we analyzed the hawthorn samples processed at different temperatures and different times by ultraviolet visible absorption spectrum and LC-MS technologies combined with multivariate statistical analysis. It was revealed for the first time that thermal processing could greatly change the ultraviolet visible absorption spectra and chemical profiles of hawthorn even with heat treatment at 130°C for 10 minutes. And the ultraviolet visible absorption spectrum, especially the ratio value (R_{A500 nm/400 nm} ), was a descriptive and qualitative indicator of heating degree for the thermal processing at the macroscopic level. Several components, such as hyperoside, chlorogenic acid, quercetin and apigenin, decreased or increased in content during the processing, and they could be utilized as the chemical quality markers. The proposed quality markers for heat-processed hawthorn will be helpful for further optimizing the processing conditions of hawthorn. This article is protected by copyright. All rights reserved.

Increased SPHK1 and HAS2 Expressions Correlate to Poor Prognosis in Pancreatic Cancer

OBJECTIVE

SPHK1 and HAS2 have been reported to play important roles in tumorigenesis and development. However, their expression and prognostic value in pancreatic cancer (PC) remain unclear. This study is aimed at investigating the expression of SPHK1 and HAS2 on the prognosis of pancreatic cancer.

MATERIALS AND METHODS

The expression of SPHK1 and HAS2 in pancreatic cancer tissues was analyzed through TCGA and GTEx databases and validated by qRT-PCR and Western blot in pancreatic cancer cell lines. χ 2 test was used to explore the correlation of the SPHK1 and HAS2 expressions with clinical characteristics. Kaplan-Meier survival analysis and ROC curve were used to evaluate the prognostic and diagnostic roles of SPHK1 and HAS2 in pancreatic cancer. Additionally, Spearman correlation analysis was applied to assess the correlation between the SPHK1 and HAS2 in pancreatic cancer. GO analysis and KEGG analysis were applied to explore the possible signaling pathway that SPHK1 and HAS2 coregulated genes mediated.

RESULTS

The expression of SPHK1 and HAS2 was markedly upregulated in pancreatic cancer tissue and cell lines, respectively. Furthermore, there was a significant positive correlation between SPHK1 and HAS2 expressions. ROC curves showed that SPHK1 combine with HAS2 has good diagnostic value in pancreatic cancer patients with 85% sensitivity and 99.4% specificity. Kaplan-Meier analysis showed that increased expression of SPHK1 and HAS2 was significantly associated with short overall survival (OS) of pancreatic cancer patients. GO and KEGG results revealed that SPHK1 and HAS2 mainly involved cell proliferation and invasion mediated by extracellular matrix- (ECM-) receptor interaction, focal adhesion, and PI3K-AKT signaling pathways.

CONCLUSIONS

Overexpression of SPHK1 and HAS2 could be important markers for the prognosis of pancreatic cancer.

Metabolomic Detection Between Pancreatic Cancer and Liver Metastasis Nude Mouse Models Constructed by Using the PANC1-KAI1/CD82 Cell Line

Background: Pancreatic cancer (PC) has a poor prognosis and is prone to liver metastasis. The KAI1/CD₈₂ gene inhibits PC metastasis. This study aimed to explore differential metabolites and enrich the pathways in serum samples between PC and liver metastasis nude mouse models stably expressing KAI1/CD₈₂. Methods: KAI1/CD₈₂-PLV-EF1α-MCS-IRES-Puro vector and PANC1 cell line stably expressing KAI1/CD₈₂ were constructed for the first time. This cell line was used to construct 3 PC nude mouse models and 3 liver metastasis nude mouse models. The different metabolites and Kyoto encyclopedia of genes and genomes (KEGG) and human metabolome database (HMDB) enrichment pathways were analyzed using the serum samples of the 2 groups of nude mouse models on the basis of untargeted ultra-performance liquid chromatography-tandem mass spectrometry platform. Results: KAI1/CD₈₂-PLV-EF1α-MCS-IRES-Puro vector and PANC1 cell line stably expressing KAI1/CD₈₂ were constructed successfully, and all nude mouse models survived and developed cancers. Among the 1233 metabolites detected, 18 metabolites (9 upregulated and 9 downregulated) showed differences. In agreement with the literature data, the most significant differences between both groups were found in the levels of bile acids (taurocholic acid, chenodeoxycholic acid), glycine, prostaglandin E2, vitamin D, guanosine monophosphate, and inosine. Bile recreation, primary bile acid biosynthesis, and purine metabolism KEGG pathways and a series of HMDB pathways (P < .05) contained differential metabolites that may be associated with liver metastasis from PC. However, the importance of these metabolites on PC liver metastases remains to be elucidated. Conclusions: Our findings suggested that the metabolomic approach may be a useful method to detect potential biomarkers in PC.

A unique urinary metabolomic signature for the detection of pancreatic ductal adenocarcinoma

Our study aimed to identify a urinary metabolite panel for the detection/diagnosis of pancreatic ductal adenocarcinoma (PDAC). PDAC continues to have poor survival outcomes. One of the major reasons for poor prognosis is the advanced stage of the disease at diagnosis. Hence, identification of a novel and cost-effective biomarker signature for early detection/diagnosis of PDAC could lead to better survival outcomes. Untargeted metabolomics was employed to identify a novel metabolite-based biomarker signature for PDAC diagnosis. Urinary metabolites from 92 PDAC patients (56 discovery cohort and 36 validation cohort) were compared with 56 healthy volunteers using ¹ H nuclear magnetic resonance spectroscopy. Multivariate (partial-least squares discriminate analysis) and univariate (Mann-Whitney's U-test) analyses were performed to identify a metabolite panel which can be used to detect PDAC. The selected metabolites were further validated for their diagnostic potential using the area under the receiver operating characteristic (AUROC) curve. Statistical analysis identified a six-metabolite panel (trigonelline, glycolate, hippurate, creatine, myoinositol and hydroxyacetone), which demonstrated high potential to diagnose PDAC, with AUROC of 0.933 and 0.864 in the discovery and validation cohort, respectively. Notably, the identified panel also demonstrated very high potential to diagnose early-stage (I and II) PDAC patients with AUROC of 0.897. These results demonstrate that the selected metabolite signature could be used to detect PDAC and will pave the way for the development of a urinary test for detection/diagnosis of PDAC.

Immune Signature Against Plasmodium falciparum Antigens Predicts Clinical Immunity in Distinct Malaria Endemic Communities

A large body of evidence supports the role of antibodies directed against the Plasmodium spp. parasite in the development of naturally acquired immunity to malaria, however an antigen signature capable of predicting protective immunity against Plasmodium remains to be identified. Key challenges for the identification of a predictive immune signature include the high dimensionality of data produced by high-throughput technologies and the limitation of standard statistical tests in accounting for synergetic interactions between immune responses to multiple targets. In this study, using samples collected from young children in Ghana at multiple time points during a longitudinal study, we adapted a predictive modeling framework which combines feature selection and machine learning techniques to identify an antigen signature of clinical immunity to malaria. Our results show that an individual's immune status can be accurately predicted by measuring antibody responses to a small defined set of 15 target antigens. We further demonstrate that the identified immune signature is highly versatile and capable of providing precise and accurate estimates of clinical protection from malaria in an independent geographic community. Our findings pave the way for the development of a robust point-of-care test to identify individuals at high risk of disease and which could be applied to monitor the impact of vaccinations and other interventions. This approach could be also translated to biomarker discovery for other infectious diseases.

Prognostic value of pre-treatment peripheral blood markers in pancreatic ductal adenocarcinoma and their association with S100A4 expression in tumor tissue

The aims of the present study were to clarify the prognostic value of peripheral blood variables in patients with pancreatic ductal adenocarcinoma (PDAC), including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and lymphocyte-to-monocyte ratio (LMR), and to determine the association between these variables and S100 calcium-binding protein A4 (S100A4) expression in tumor tissue, which is another prognostic factor for PDAC. Patients with PDAC were recruited at the Tianjin Medical University Cancer Institute and Hospital (Tianjin, China) between December 2008 and December 2014. A retrospective analysis was performed based on the recorded pre-treatment hematological parameters and clinical data. The prognostic value of NLR, PLR and LMR was examined. The association between these variables and S100A4 tissue expression was analyzed. Descriptive statistics and χ2 analyses were used in the present study. The median overall survival (OS) time of patients with PDAC was 9 months (range, 1-32 months). Univariate analysis revealed that NLR, LMR, carbohydrate antigen 19-9, surgery, chemotherapy, stage at diagnosis, tumor grade and age significantly affected OS. Although PLR exhibited no significant effects on OS, NLR and LMR were independent prognostic factors according to the multivariate analysis. Unpaired Student's t-test revealed differences between S100A4 expression and NLR, PLR and LMR. The results of the present study indicated that low NLR and high LMR were associated with a favorable prognosis in patients with PDAC. As a simply obtained and widely available index at diagnosis, NLR and LMR may become a novel predictive and classifying marker for PDAC in the clinical setting.

A Prospective Targeted Serum Metabolomics Study of Pancreatic Cancer in Postmenopausal Women

To examine the association between metabolic deregulation and pancreatic cancer, we conducted a two-stage case-control targeted metabolomics study using prediagnostic sera collected one year before diagnosis in the Women's Health Initiative study. We used the LC/MS to quantitate 470 metabolites in 30 matched case/control pairs. From 180 detectable metabolites, we selected 14 metabolites to be validated in additional 18 matched case/control pairs. We used the paired t test to compare the concentrations of each metabolite between cases and controls and used the log fold change (FC) to indicate the magnitude of difference. FDR adjusted q-value < 0.25 was indicated statistically significant. Logistic regression model and ROC curve analysis were used to evaluate the clinical utility of the metabolites. Among 30 case/control pairs, 1-methyl-l-tryptophan (L-1MT) was significantly lower in the cases than in the controls (log₂ FC = -0.35; q-value = 0.03). The area under the ROC curve was 0.83 in the discrimination analysis based on the levels of L-1MT, acadesine, and aspartic acid. None of the metabolites was validated in additional independent 18 case/control pairs. No significant association was found between the examined metabolites and undiagnosed pancreatic cancer.

Circulating pancreatic cancer exosomal RNAs for detection of pancreatic cancer

Diagnostic biomarkers for the early diagnosis of pancreatic cancer are needed to improve prognosis for this disease. The aim of this study was to investigate differences in the expression of four messenger RNAs (mRNAs: CCDC88A,ARF6, Vav3, and WASF2) and five small nucleolar RNAs (snoRNAs: SNORA14B,SNORA18,SNORA25,SNORA74A, and SNORD22) in serum of patients with pancreatic cancer and control participants for use in the diagnosis of pancreatic cancer. Results were compared with the expression of sialylated Lewis (a) blood group antigen CA19‐9, the standard clinical tumor biomarker. Reverse transcription quantitative real‐time PCR showed that all of the mRNAs and snoRNAs, except CCDC88A, were encapsulated in exosomes and secreted from cultured pancreatic cancer cells, and present in cell culture medium. In a discovery‐stage clinical study involving 27 pancreatic cancer patients and 13 controls, the area under the receiver operating characteristic curve (AUC) of two mRNAs (WASF2 and ARF6) and two snoRNAs (SNORA74A and SNORA25) was > 0.9 for distinguishing pancreatic cancer patients from controls; the AUC of CA19‐9 was 0.897. Comparing serum levels of WASF2,ARF6,SNORA74A,SNORA25, and CA19‐9 revealed that levels of WASF2 were the most highly correlated with the risk of pancreatic cancer. The AUCs of WASF2,ARF6,SNORA74A, and SNORA25 in serum from patients in the early stages of pancreatic cancer (stages 0, I, and IIA) were > 0.9, compared with an AUC of 0.93 for the level of CA19‐9. The results of this study suggest that WASF2,ARF6,SNORA74A, and SNORA25 may be useful tools for the early detection of pancreatic cancer. Monitoring serum levels of WASF2 mRNA may be particularly useful, as it was the most highly correlated with pancreatic cancer risk.

Bayesian state-space modeling in gene expression data analysis: An application with biomarker prediction

Background and ObjectiveBayesian State Space models are recent advancement in stochastic modeling which capture the randomness of a hidden background process by scrutinizing the prior knowledge and likelihood of observed data. This article elucidate the scope of Bayesian state space modeling on predicting the future expression values of a longitudinal micro array data. MethodsThe study conveniently makes use of longitudinally collected clinical trial data (GSE30531) from NCBI Gene Expression Omnibus (GEO) data repository. Multiple testing methodology using t-test is used for selecting differentially expressed genes between groups for fitting the model. The parameter values of the predictive model and future expression levels are estimated by drawing samples from the posterior joint distribution using a stochastic Markov Chain Monte Carlo (MCMC) algorithm which relies on Gibbs Sampling. The study also made an attempt to get estimates and its 95% Credible Interval through assumptions of different covariance structures like Variance Components, First order Auto Regressive and Unstructured variance-covariance structure to showcase the flexibility of the algorithm. Results72 Distinct genes with significantly different expression levels where selected for model fitting. Parameter estimates showed almost similar trends under different covariance structure assumption. Cross tabulation of gene frequencies having minimum credible interval under each covariance structure and study group showed a significant P value of 0.02. ConclusionsPresent study reveals that Bayesian state space models can be effectively used to explain and predict a complex data like gene expression data.

Translational genomics in pancreatic ductal adenocarcinoma: A review with re-analysis of TCGA dataset

Malignancy of the pancreas is a leading cause of cancer-related mortality, with the highest case-fatality of all cancers. Nevertheless, the lack of sensitive biomarkers and presence of biological heterogeneity precludes its early detection and effective treatment. The recent introduction of next-generation sequencing allows characterization of multiple driver mutations at genome- and exome-wide levels. Sequencing of DNA and RNA from circulating tumour cells has also opened an exciting era of non-invasive procedures for tumour detection and prognostication. This massively-parallel sequencing technology has uncovered the previously obscure molecular mechanisms, providing clues for better stratification of patients and identification of druggable targets for the disease. Identification of active oncogenic pathways and gene-gene interactions may reveal oncogene addiction and synthetic lethality. Relevant findings can be extrapolated to develop targeted and personalized therapeutic interventions. In addition to known mutational events, the role of chromosomal rearrangements in pancreatic neoplasms is gradually uncovered. Coupled with bioinformatics pipelines and epidemiological analyses, a better framework for risk stratification and prognostication of pancreatic cancer will be possible in the near future. In this review, we discuss how translational genomic studies facilitate our understanding of pathobiology, and development of novel diagnostics and therapeutics for pancreatic ductal adenocarcinoma with emphases on whole genome sequencing, whole exome sequencing, and liquid biopsies. We have also re-analyzed The Cancer Genome Atlas (TCGA) dataset to look for genetic features associated with altered survival in patients with pancreatic ductal adenocarcinoma.

Metabolomics approaches in pancreatic adenocarcinoma: tumor metabolism profiling predicts clinical outcome of patients

BACKGROUND

Pancreatic adenocarcinomas (PAs) have very poor prognoses even when surgery is possible. Currently, there are no tissular biomarkers to predict long-term survival in patients with PA. The aims of this study were to (1) describe the metabolome of pancreatic parenchyma (PP) and PA, (2) determine the impact of neoadjuvant chemotherapy on PP and PA, and (3) find tissue metabolic biomarkers associated with long-term survivors, using metabolomics analysis.

METHODS

1H high-resolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR) spectroscopy using intact tissues was applied to analyze metabolites in PP tissue samples (n = 17) and intact tumor samples (n = 106), obtained from 106 patients undergoing surgical resection for PA.

RESULTS

An orthogonal partial least square-discriminant analysis (OPLS-DA) showed a clear distinction between PP and PA. Higher concentrations of myo-inositol and glycerol were shown in PP, whereas higher levels of glucose, ascorbate, ethanolamine, lactate, and taurine were revealed in PA. Among those metabolites, one of them was particularly obvious in the distinction between long-term and short-term survivors. A high ethanolamine level was associated with worse survival. The impact of neoadjuvant chemotherapy was higher on PA than on PP.

CONCLUSIONS

This study shows that HRMAS NMR spectroscopy using intact tissue provides important and solid information in the characterization of PA. Metabolomics profiling can also predict long-term survival: the assessment of ethanolamine concentration can be clinically relevant as a single metabolic biomarker. This information can be obtained in 20 min, during surgery, to distinguish long-term from short-term survival.

Sulfatase-2: a prognostic biomarker and candidate therapeutic target in patients with pancreatic ductal adenocarcinoma

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is the fifth most common cause of cancer death in the UK. Its poor prognosis is attributed to late detection and limited therapeutic options. Expression of SULF2, an endosulfatase that modulates heparan sulfate proteoglycan 6-O-sulfation and is reportedly tumourigenic in different types of cancer, was investigated.

METHODS

SULF2 expression was determined immunohistochemically in archival surgical resection tissue sections from 93 patients with a confirmed histological diagnosis of PDAC between 2002 and 2008 followed for a median of 9 years. Relationships with clinico-pathological parameters and patient survival were explored.

RESULTS

The majority of PDACs showed positive SULF2 staining in tumour cells and intratumoural or tumour-adjacent stroma. Greater than 25% SULF2-positive tumour cells was present in 60% of cancers and correlated with tumour stage (P=0.002) and perineural invasion (P=0.024). SULF2 intensity was scored moderate or strong in 81% of cancers and positively correlated with vascular invasion (P=0.015). High SULF2 expression, defined as >50% SULF2-positive tumour cells and strong SULF2 staining, was associated with shorter time to radiological progression (P=0.018, HR 1.98, CI 1.13-3.47). Similarly, by multivariate analysis, high SULF2 expression was independently associated with poorer survival (P=0.004, HR 2.10, CI 1.26-3.54), with a median survival of 11 months vs 21 months for lower PDAC SULF2.

CONCLUSIONS

Elevated SULF2 in PDAC was associated with advanced tumour stage, vascular invasion, shorter interval to radiological progression and shorter overall survival. SULF2 may have roles as a prognostic biomarker and as a therapeutic target for patients with PDAC.

Plasma biomarker for detection of early stage pancreatic cancer and risk factors for pancreatic malignancy using antibodies for apolipoprotein-AII isoforms

We recently reported that circulating apolipoprotein AII (apoAII) isoforms apoAII-ATQ/AT (C-terminal truncations of the apoAII homo-dimer) decline significantly in pancreatic cancer and thus might serve as plasma biomarkers for the early detection of this disease. We report here the development of novel enzyme-linked immunosorbent assays (ELISAs) for measurement of apoAII-ATQ/AT and their clinical applicability for early detection of pancreatic cancer. Plasma and serum concentrations of apoAII-ATQ/AT were measured in three independent cohorts, which comprised healthy control subjects and patients with pancreatic cancer and gastroenterologic diseases (n = 1156). These cohorts included 151 cases of stage I/II pancreatic cancer. ApoAII-ATQ/AT not only distinguished the early stages of pancreatic cancer from healthy controls but also identified patients at high risk for pancreatic malignancy. AUC values of apoAII-ATQ/AT to detect early stage pancreatic cancer were higher than those of CA19–9 in all independent cohorts. ApoAII-ATQ/AT is a potential biomarker for screening patients for the early stage of pancreatic cancer and identifying patients at risk for pancreatic malignancy (161 words).

Proteomic approaches to identify circulating biomarkers in patients with abdominal aortic aneurysm

Abdominal aortic aneurysm (AAA) is a common condition with high mortality when ruptured. Most clinicians agree that small AAAs are best managed by ultrasonographic surveillance. However, it has been stated in recent reviews that a serum/plasma biomarker that predicts AAA rupture risk would be a powerful tool in stratifying patients with small AAAs. Identification of such circulating biomarkers with traditional hypothesis driven studies has been unsuccessful. In this review we summarize six studies using different proteomic approaches to find new, potential plasma AAA biomarker candidates. In conclusion, by using proteomic approaches novel potential plasma biomarkers for AAA have been identified.