Pilot study using SELDI-TOF-MS based proteomic profile for the identification of diagnostic biomarkers of thyroid proliferative diseases

Biomarkers for thyroid cancer (TCa) lack specificity. To develop TCa specific biomarkers, SELDI-TOF-MS was used to examine the proteomic profile of biopsies obtained from papillary TCa along with adjacent normal tissue. Sixty-three potential biomarkers were categorized by univariate analysis into single biomarker candidates and segregated by multivariate analysis into normal and cancerous groups. Our studies demonstrate the sensitivity and reproducibility of this approach to detect biomarkers for TCa.     

Diagnostic application of serum proteomic patterns in gastric cancer patients by ProteinChip surface-enhanced laser desorption/ionization time-of-flight mass spectrometry

Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) is one of thecurrently used techniques to identify biomarkers for cancers. This study was planned to establish a system to accurately distinguish gastric cancer patients by using SELDI-TOF-MS. A total of 100 serum samples obtained from 60 individuals with gastric cancer and 40 healthy individuals were screened. Protein expression profiles were expressed on CM10 ProteinChip arrays and analyzed. Peak intensities were analyzed with the Biomarker Wizard software to identify peaks showing significantly different intensities between normal and cancer groups. Classification analysis and construction of decision trees were done with the Biomarker Pattern software 5.0. Seventeen protein peaks showed significant differences between the two groups. The decision tree which gave the highest discrimination included four peaks at mass 5,919, 8,583, 10,286, and 13,758 as splitters. The sensitivity and specificity for classification of the decision tree were 96.7% (58/60) and 97.5% (39/40), respectively. When the protein biomarker pattern was tested on a blinded test set, it yielded a sensitivity of 93.3% (28/30) and a specificity of 90% (18/20). These results suggest that serum protein profiling by the SELDI system may distinguish gastric cancer patients from healthy controls with relatively high sensitivity and specificity.     

SELDI-TOF-MS筛选慢性阻塞性肺疾病血清标志蛋白的初步研究

目的:利用表面增强激光解吸电离飞行时间质谱技术(SELDI-TOF-MS)筛选慢性阻塞性肺疾病(COPD)血清特异标志物。方法:应用SELDI-TOF-MS技术检测30例COPD稳定期患者和30例健康对照者血清蛋白指纹图谱,采用Biomarker pattern软件进行分析,建立COPD的诊断模型。结果:COPD患者血清蛋白图谱与对照组相比,在相对分子质量2000-15 000范围内共检测到75个蛋白峰,发现19个有统计学差异的蛋白峰(P0.05)。通过对COPD组与对照组间的数据作进一步分析,经BPS软件分析,建立质荷比(M/Z)3 167、4 645的差异蛋白组成的诊断模型,其诊断敏感度为96.67%,特异度为96.67%。结论:SELDI-TOF-MS技术是一种快速、简单易行、用量少和高通量的分析方法。能直接筛选出COPD血清中特异表达标志物,用特异表达标志物建立的诊断模型能有效区分COPD患者与健康对照者,有望成为COPD诊断的辅助指标。     

表面增强激光解吸电离飞行时间质谱技术筛查肺癌血清特异性蛋白质及其临床意义

目的:探讨用表面增强激光解吸电离飞行时间质谱(SELDI-TOF-MS)技术筛查肺癌血清特异性蛋白质的临床意义。方法:应用SELDI-TOF-MS对35例正常对照组、43例治疗前肺癌病人的血清样品进行蛋白质指纹图谱测定,用BioMarker Wizard 3．01及BioMarker Parrern System 5．01分析软件对测得的数据进行处理及建立诊断模型。结果:共检测到251个蛋白质峰,筛选出差异蛋白质峰11个,以质荷比(m/z)分别为M2799_26,M3227_41,M5739_70和M8164_30的4个蛋白质峰为依据组合构建分类决策树模型,分出5个终节点。决策树模型的原始判别总准确率为91．0%(71/78),敏感性为88．4%(38/43),特异性为94．3%(33/35);交叉验证总准确率为85．9%(67/78),敏感性为88．4%(38/43),特异性为82．9%(29/35)。结论:SELDI-TOF-MS在肺癌血清特异性蛋白质的筛选及诊断模型的建立有一定的临床意义。     

血清蛋白质指纹图谱模型在子宫内膜异位症诊断中的应用

用表面加强激光解析电离飞行时间质谱(SELDI-TOF-MS)和蛋白质芯片检测子宫内膜异位症(endometriosis,EM)患者血清蛋白质指纹图谱,探讨诊断模型在EM诊断中的临床应用价值。用SELDI-TOF-MS技术和H4蛋白质芯片检测16例EM和16例正常女性的血清蛋白质指纹图谱,并建立诊断模型。然后,对16名健康人和16例EM患者样本进行盲法测试验证该模型。筛选出4个有明显表达差异的蛋白质,其质荷比(m/z)分别为8141、6096、5894、3269。建立的诊断模型对EM检测的灵敏度为87.5%(14/16),特异性为93.75%(15/16),总准确率为90.625%(29/32)。SELDI-TOF-MS对小样本的EM诊断具有较高的敏感性和特异性,在EM的诊断及标志物筛选等方面具有较好的诊断价值。     

New potential biomarkers in the diagnosis of esophageal squamous cell carcinoma

Objective: To analyse the alterations of serum proteins in cases of esophageal squamous cell carcinoma (ESCC) in order to screen and validate serum marker patterns for the diagnosis of ESCC in the high-risk populations of Xinjiang, China. Methods: The serum proteomic patterns of 188 cases, including 139 patients with ESCC (54 Uygur, 45 Kazakh and 40 Han subjects) and 49 sex- and age-matched healthy controls, were detected using the SELDI-TOF-MS (surface-enhanced laser desorption/ionization–time of flight–mass spectrometry) technology with the CM10 ProteinChip. Differences in protein peaks between patients with ESCC and controls were analysed using the Biomarker Pattern Software, and a primary diagnosis model of ESCC was developed and validated with SVM (support vector machines). This model was further evaluated by a large-scale blind test. Results: Two hundred and eighty-three protein peaks were detected within the molecular range of 0–20?kDa, among which, 140 peaks were significantly different between ESCC cases and controls (p?m/z 5667, 5709, 5876, 5979, 6043 and 6102) was established with a sensitivity of 97.12% and a specificity of 83.87%. The large-scale blind test generated a sensitivity of 91.43% and a specificity of 88.89%. Conclusions: The differential protein peaks analysed by SELDI-TOF-MS may contain promising serum biomarkers for screening ESCC. The diagnostic model which combined only six protein peaks had a satisfactory discriminatory power. The model should be further evaluated in other populations of ESCC patients and tested against controls. The nature and function of the discriminating proteins have yet to be elucidated.     

The potential value of the demonstration of thyroglobulin by immunoperoxidase techniques in fine needle aspiration cytology

The peroxidase-antiperoxidase method for the demonstration of thyroglobulin was performed on 30 fine needle aspiration smears. All of the benign thyroid lesions plus the papillary and follicular carcinomas of the thyroid were positive for thyroglobulin. Two cases of metastatic thyroid carcinoma in lymph nodes were also positive. Medullary carcinoma of the thyroid and ten control smears from various other tissues were negative. This proves the sensitivity and the specificity of the method, which may be used in routine cytology and may add to the accuracy of diagnosing metastatic tumors suspected of being of thyroid origin. The intranuclear cytoplasmic vacuoles in papillary carcinoma of the thyroid were negative for thyroglobulin. This unexpected finding is demonstrated, and a possible explanation is offered.     

Genome-wide methylation profiling reveals Zinc finger protein 516 (ZNF516) and FK-506-binding protein 6 (FKBP6) promoters frequently methylated in cervical neoplasia,associated with HPV status and ethnicity in a Chilean population

Cervical cancer is a major health concern among women in Latin America due to its high incidence and mortality. Therefore, the discovery of molecular markers for cervical cancer screening and triage is imperative. The aim of this study was to use a genome wide DNA methylation approach to identify novel methylation biomarkers in cervical cancer. DNA from normal cervical mucosa and cervical cancer tissue samples from Chile was enriched with Methylated DNA Immunoprecipitation (MeDIP), hybridized to oligonucleotide methylation microarrays and analyzed with a stringent bioinformatics pipeline to identify differentially methylated regions (DMRs) as candidate biomarkers. Quantitative Methylation Specific PCR (qMSP) was used to study promoter methylation of candidate DMRs in clinical samples from two independent cohorts. HPV detection and genotyping were performed by Reverse Line Blot analysis. Bioinformatics analysis revealed GGTLA4, FKBP6, ZNF516, SAP130, and INTS1 to be differentially methylated in cancer and normal tissues in the Discovery cohort. In the Validation cohort FKBP6 promoter methylation had 73% sensitivity and 80% specificity (AUC = 0.80). ZNF516 promoter methylation was the best biomarker, with both sensitivity and specificity of 90% (AUC = 0.92), results subsequently corroborated in a Prevalence cohort. Together, ZNF516 and FKBP6 exhibited a sensitivity of 84% and specificity of 81%, when considering both cohorts. Our genome wide DNA methylation assessment approach (MeDIP-chip) successfully identified novel biomarkers that differentiate between cervical cancer and normal samples, after adjusting for age and HPV status. These biomarkers need to be further explored in case-control and prospective cohorts to validate them as cervical cancer biomarkers.     

Genome-wide methylation profiling reveals Zinc finger protein 516 (ZNF516) and FK-506-binding protein 6 (FKBP6) promoters frequently methylated in cervical neoplasia,associated with HPV status and ethnicity in a Chilean population

Cervical cancer is a major health concern among women in Latin America due to its high incidence and mortality. Therefore, the discovery of molecular markers for cervical cancer screening and triage is imperative. The aim of this study was to use a genome wide DNA methylation approach to identify novel methylation biomarkers in cervical cancer. DNA from normal cervical mucosa and cervical cancer tissue samples from Chile was enriched with Methylated DNA Immunoprecipitation (MeDIP), hybridized to oligonucleotide methylation microarrays and analyzed with a stringent bioinformatics pipeline to identify differentially methylated regions (DMRs) as candidate biomarkers. Quantitative Methylation Specific PCR (qMSP) was used to study promoter methylation of candidate DMRs in clinical samples from two independent cohorts. HPV detection and genotyping were performed by Reverse Line Blot analysis. Bioinformatics analysis revealed GGTLA4, FKBP6, ZNF516, SAP130, and INTS1 to be differentially methylated in cancer and normal tissues in the Discovery cohort. In the Validation cohort FKBP6 promoter methylation had 73% sensitivity and 80% specificity (AUC = 0.80). ZNF516 promoter methylation was the best biomarker, with both sensitivity and specificity of 90% (AUC = 0.92), results subsequently corroborated in a Prevalence cohort. Together, ZNF516 and FKBP6 exhibited a sensitivity of 84% and specificity of 81%, when considering both cohorts. Our genome wide DNA methylation assessment approach (MeDIP-chip) successfully identified novel biomarkers that differentiate between cervical cancer and normal samples, after adjusting for age and HPV status. These biomarkers need to be further explored in case-control and prospective cohorts to validate them as cervical cancer biomarkers.     

SELDI-TOF-MS Proteomic Profiling of Serum,Urine, and Amniotic Fluid in Neural Tube Defects

Neural tube defects (NTDs) are common birth defects, whose specific biomarkers are needed. The purpose of this pilot study is to determine whether protein profiling in NTD-mothers differ from normal controls using SELDI-TOF-MS. ProteinChip Biomarker System was used to evaluate 82 maternal serum samples, 78 urine samples and 76 amniotic fluid samples. The validity of classification tree was then challenged with a blind test set including another 20 NTD-mothers and 18 controls in serum samples, and another 19 NTD-mothers and 17 controls in urine samples, and another 20 NTD-mothers and 17 controls in amniotic fluid samples. Eight proteins detected in serum samples were up-regulated and four proteins were down-regulated in the NTD group. Four proteins detected in urine samples were up-regulated and one protein was down-regulated in the NTD group. Six proteins detected in amniotic fluid samples were up-regulated and one protein was down-regulated in the NTD group. The classification tree for serum samples separated NTDs from healthy individuals, achieving a sensitivity of 91% and a specificity of 97% in the training set, and achieving a sensitivity of 90% and a specificity of 97% and a positive predictive value of 95% in the test set. The classification tree for urine samples separated NTDs from controls, achieving a sensitivity of 95% and a specificity of 94% in the training set, and achieving a sensitivity of 89% and a specificity of 82% and a positive predictive value of 85% in the test set. The classification tree for amniotic fluid samples separated NTDs from controls, achieving a sensitivity of 93% and a specificity of 89% in the training set, and achieving a sensitivity of 90% and a specificity of 88% and a positive predictive value of 90% in the test set. These suggest that SELDI-TOF-MS is an additional method for NTDs pregnancies detection.     

Telomerase activity in thyroid fine needle aspirates

OBJECTIVE: To study the utility of telomerase activity (TA) detection in thyroid fine needle aspirates (FNAs). STUDY DESIGN: One hundred two thyroid nodules were studied: 70 in FNA samples and 32 in frozen tumors. From among FNA samples, there were 57 nodules of the goiter, 1 adenoma, 7 papillary carcinomas, 1 medullary carcinoma and 1 lymphoma. Three cases of thyroiditis were excluded. The 32 frozen tissue tumors consisted of 5 follicular adenomas, 4 follicular carcinomas and 23 papillary carcinomas. TA was analyzed by Telomerase PCR ELISA (Roche Diagnostics, Indianapolis, Indiana, U.S.A.). RESULTS: TA was negative in the 57 nonneoplastic nodules and 6 follicular adenomas and positive in 3 of 4 follicular carcinomas and 10 of 30 papillary carcinomas. TA sensitivity was 41.4% and specificity 100%. Sensitivity for malignancy was higher (85.7%) in FNAs than in TA. CONCLUSION: TA seems highly specific for neoplasms of the thyroid. Further studies are needed to confirm whether TA detection could contribute to identifying neoplasms when FNAs are inconclusive for malignancy and in cases of scanty material.     

Improving Detection Accuracy of Lung Cancer Serum Proteomic Profiling via Two-Stage Training Process

Background  

Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDI-TOF-MS) is a frequently used technique for cancer biomarker research. The specificity of biomarkers detected by SELDI can be influenced by concomitant inflammation. This study aimed to increase detection accuracy using a two-stage analysis process.     

Posters. P13 Cytohistological correlation in 212 FNAs of thyroid: analysis of discordant cases

Aim To detect major pitafalls in thyroid FNA and to confirm its in a clinical sensitivity and specificity. Methods A total of 9251 fine needle aspirations biopsy carried out at Bellaria Hospital in Bologna from 1991 to 2000 by a pathologist in the FNA Clinic or by a clinician under ultrasonic guidance using a small needle (25–27 G); at least two passes have been made for each nodule. The specimen was considered satisfactory if at least five groups of follicular cells with at least 10 cells each, were seen. The cytological results were tiered in a four categories classification: inadequate, negative, suspicious and positive. Cyto‐histological correlations were available in 212 cases: 127 benign lesions and 85 malignant lesions. An analysis of false positive cases and false negative cases was performed and discordant case reviewed according to the flowing criteria: architecture, cellularity, colloid, pseudoinclusions, nuclear groovings, chromatin pattern, nuclear membrane, cytoplasm, naked nuclei and lymphocytes. Results Diagnostic distribution in 9251 FNAs from the thyroid: 88.6% negatives, 2.8 suspicious, 2.4% positives and 6.2% inadequates. Specificity was 85.8% and sensitivity was 78.8%. Among the 18 false negative cases eight were papillary microcarcinomas, four papillary carcinomas, five follicular carcinoma and one a Hurtle cell carcinoma. Four false positive cases were found: three reported as papillary carcinomas and one as carcinoma NOS. Review of false positives showed that in three cases the colloid was fluid, in three cases nuclear grooving was rare or absent, in two cases degenerative vacuoles at MGG were interpreted as nuclear inclusions and in three cases benign naked nuclei were present in the background. Review of false negatives confirmed lack of malignant features in 13 (eight papillary microcarcinomas and five follicular carcinomas), five were interpretation errors (three papillary carcinomas, one follicular, one Hurtle cell). Conclusion FNAC of the thyroid is a sensitive and specific method of assessment for thyroid nodules but false negative and false positive cases do occur. Use of all and only few criteria enhances diagnostic accuracy.     

Dipeptidyl peptidase IV expression in thyroid cytology: retrospective histologically confirmed study

Fine needle aspiration cytology (FNAC) of the thyroid gland is a well-established method. However, it has inherent limitations, especially in the diagnosis of follicular and oncocytic tumours and in distinguishing between nuclear atypia in colloid goitre with regressive changes and cystic papillary carcinoma. The aim of our study was to evaluate dipeptidyl peptidase IV (DPP IV) as a marker of malignancy in FNAC. We tested 254 thyroid specimens (intraoperative imprint smears) for DPP IV. The sensitivity was 71%, the specificity was 96%, and the diagnostic accuracy was 93%, respectively, with a threshold of 50% of positive cells. To the best of our knowledge it is the largest histologically confirmed study reported in the literature. We suggest the assessment of DPP IV as an adjunct diagnostic marker of malignancy in thyroid specimens suspicious of papillary carcinoma. However, the value of the marker in follicular lesions is very limited.     

Application of Artificial Intelligence/Machine Vision & Learning for the Development of a Live Single-cell Phenotypic Biomarker Test to Predict Prostate Cancer Tumor Aggressiveness

To assess the usefulness and applications of machine vision (MV) and machine learning (ML) techniques that have been used to develop a single cell-based phenotypic (live and fixed biomarkers) platform that correlates with tumor biological aggressiveness and risk stratification, 100 fresh prostate samples were acquired, and areas of prostate cancer were determined by post-surgery pathology reports logged by an independent pathologist. The prostate samples were dissociated into single-cell suspensions in the presence of an extracellular matrix formulation. These samples were analyzed via live-cell microscopy. Dynamic and fixed phenotypic biomarkers per cell were quantified using objective MV software and ML algorithms. The predictive nature of the ML algorithms was developed in two stages. First, random forest (RF) algorithms were developed using 70% of the samples. The developed algorithms were then tested for their predictive performance using the blinded test dataset that contained 30% of the samples in the second stage. Based on the ROC (receiver operating characteristic) curve analysis, thresholds were set to maximize both sensitivity and specificity. We determined the sensitivity and specificity of the assay by comparing the algorithm-generated predictions with adverse pathologic features in the radical prostatectomy (RP) specimens. Using MV and ML algorithms, the biomarkers predictive of adverse pathology at RP were ranked and a prostate cancer patient risk stratification test was developed that distinguishes patients based on surgical adverse pathology features. The ability to identify and track large numbers of individual cells over the length of the microscopy experimental monitoring cycles, in an automated way, created a large biomarker dataset of primary biomarkers. This biomarker dataset was then interrogated with ML algorithms used to correlate with post-surgical adverse pathology findings. Algorithms were generated that predicted adverse pathology with >0.85 sensitivity and specificity and an AUC (area under the curve) of >0.85. Phenotypic biomarkers provide cellular and molecular details that are informative for predicting post-surgical adverse pathologies when considering tumor biopsy samples. Artificial intelligence ML-based approaches for cancer risk stratification are emerging as important and powerful tools to compliment current measures of risk stratification. These techniques have capabilities to address tumor heterogeneity and the molecular complexity of prostate cancer. Specifically, the phenotypic test is a novel example of leveraging biomarkers and advances in MV and ML for developing a powerful prognostic and risk-stratification tool for prostate cancer patients.     

Novel serological biomarker panel using protein microarray can distinguish active TB from latent TB infection

BackgroundRapid laboratory technologies which can effectively distinguish active tuberculosis (ATB) from controls and latent tuberculosis infection (LTBI) are lacked.The objective of this study is to explore MTB biomarkers in serum that can distinguish ATB from LTBI.MethodsWe constructed a tuberculosis protein microarray containing 64 MTB associated antigens. We then used this microarray to screen 180 serum samples, from patients with ATB and LTBI, and healthy volunteer controls. Both SAM (Significance analysis of microarrays) and ROC curve analysis were used to identify the differentially recognized biomarkers between groups. Extra 300 serum samples from patients with ATB and LTBI, and healthy volunteer controls were employed to validate the identified biomarkers using ELISA-based method.ResultsAccording to the results, the best biomarker combinations of 4 proteins (Rv1860, RV3881c, Rv2031c and Rv3803c) were selected. The biomarker panel containing these 4 proteins has reached a sensitivity of 93.3% and specificity of 97.7% for distinguishing ATB from LTBI, and a sensitivity of 86% and specificity of 97.6% for distinguishing ATB from HC.ConclusionThe biomarker combination in this study has high sensitivity and specificity in distinguishing ATB from LTBI, suggesting it is worthy for further validation in more clinical samples.     

Identification of Plasma Lipid Biomarkers for Prostate Cancer by Lipidomics and Bioinformatics

Background

Lipids have critical functions in cellular energy storage, structure and signaling. Many individual lipid molecules have been associated with the evolution of prostate cancer; however, none of them has been approved to be used as a biomarker. The aim of this study is to identify lipid molecules from hundreds plasma apparent lipid species as biomarkers for diagnosis of prostate cancer.

Methodology/Principal Findings

Using lipidomics, lipid profiling of 390 individual apparent lipid species was performed on 141 plasma samples from 105 patients with prostate cancer and 36 male controls. High throughput data generated from lipidomics were analyzed using bioinformatic and statistical methods. From 390 apparent lipid species, 35 species were demonstrated to have potential in differentiation of prostate cancer. Within the 35 species, 12 were identified as individual plasma lipid biomarkers for diagnosis of prostate cancer with a sensitivity above 80%, specificity above 50% and accuracy above 80%. Using top 15 of 35 potential biomarkers together increased predictive power dramatically in diagnosis of prostate cancer with a sensitivity of 93.6%, specificity of 90.1% and accuracy of 97.3%. Principal component analysis (PCA) and hierarchical clustering analysis (HCA) demonstrated that patient and control populations were visually separated by identified lipid biomarkers. RandomForest and 10-fold cross validation analyses demonstrated that the identified lipid biomarkers were able to predict unknown populations accurately, and this was not influenced by patient''s age and race. Three out of 13 lipid classes, phosphatidylethanolamine (PE), ether-linked phosphatidylethanolamine (ePE) and ether-linked phosphatidylcholine (ePC) could be considered as biomarkers in diagnosis of prostate cancer.

Conclusions/Significance

Using lipidomics and bioinformatic and statistical methods, we have identified a few out of hundreds plasma apparent lipid molecular species as biomarkers for diagnosis of prostate cancer with a high sensitivity, specificity and accuracy.     

Nuclear grooves in cytologic preparations. A study of the utility of this feature in the diagnosis of papillary carcinoma

The sensitivity and specificity of nuclear grooves and inclusions for papillary carcinoma was investigated in 32 touch preparations and 69 fine needle aspiration cytologic preparations of the thyroid. Ultrastructurally, these grooves and inclusions are cytoplasmic invaginations into the nucleus. Overall, 100% of the papillary carcinomas contained nuclear grooves while only 70% contained inclusions. Grooves, however, could be seen in 70% of nonpapillary neoplasms and in 56% of nonneoplastic conditions of the thyroid, albeit generally fewer in number and often not as distinct. Inclusions were present in 13% of nonpapillary neoplasms and were absent in nonneoplastic conditions. Some nuclei on cytologic preparations contain lines that are probably artifacts of chromatin alignment and do not represent true nuclear grooves. Since such lines may be indistinguishable from true grooves, grooves should be used cautiously and in conjunction with other criteria in the diagnosis of papillary carcinoma.     

Follicular variant of papillary thyroid carcinoma: diagnostic limitations of fine needle aspiration cytology

OBJECTIVE: To study the diagnostic sensitivity and specificity of fine needle aspiration cytology (FNAC) offollicular variant of papillary thyroid carcinoma (FVPTC). STUDY DESIGN: The study group consisted of 390 papillary thyroid carcinoma (PTC) cases diagnosed histologically with thyroidectomy specimens. The FNAC and histopathologic classification were compared in terms of the appearance of FVPTC and non-FVPTC statistically with the chi squared test. Also, several features of the cytologic smears of FVPTC were reviewed. RESULTS: Twelve of the 390 PTC cases were classified as FVPTC histologically. Five of the 12 cases were also reported as FVPTC in the diagnosis by FNAC and the other 7 as the usual type of PTC (UTPTC). There was 1 case classified as UVPTC histologically but FVPTC cytologically. If we use histologic diagnosis as the gold standard, the sensitivity and specificity of FNAC diagnosis of FVPTC were 42% and 83%, respectively. CONCLUSION: FNAC may be a good tool for diagnosing PTC, but it is unreliable to differentiate between FVPTC and UTPTC.     

Candidate diagnostic biomarkers for neurodevelopmental disorders in children and adolescents: a systematic review

Neurodevelopmental disorders – including attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder, communication disorders, intellectual disability, motor disorders, specific learning disorders, and tic disorders – manifest themselves early in development. Valid, reliable and broadly usable biomarkers supporting a timely diagnosis of these disorders would be highly relevant from a clinical and public health standpoint. We conducted the first systematic review of studies on candidate diagnostic biomarkers for these disorders in children and adolescents. We searched Medline and Embase + Embase Classic with terms relating to biomarkers until April 6, 2022, and conducted additional targeted searches for genome-wide association studies (GWAS) and neuroimaging or neurophysiological studies carried out by international consortia. We considered a candidate biomarker as promising if it was reported in at least two independent studies providing evidence of sensitivity and specificity of at least 80%. After screening 10,625 references, we retained 780 studies (374 biochemical, 203 neuroimaging, 133 neurophysiological and 65 neuropsychological studies, and five GWAS), including a total of approximately 120,000 cases and 176,000 controls. While the majority of the studies focused simply on associations, we could not find any biomarker for which there was evidence – from two or more studies from independent research groups, with results going into the same direction – of specificity and sensitivity of at least 80%. Other important metrics to assess the validity of a candidate biomarker, such as positive predictive value and negative predictive value, were infrequently reported. Limitations of the currently available studies include mostly small sample size, heterogeneous approaches and candidate biomarker targets, undue focus on single instead of joint biomarker signatures, and incomplete accounting for potential confounding factors. Future multivariable and multi-level approaches may be best suited to find valid candidate biomarkers, which will then need to be validated in external, independent samples and then, importantly, tested in terms of feasibility and cost-effectiveness, before they can be implemented in daily clinical practice.