An ultrasensitive electrochemical biosensor with dual-amplification mode and enzyme-deposited silver for detection of miR-205-5p.

An electrochemical biosensor based on dual-amplified nucleic acid mode and biocatalytic silver deposition was constructed using catalytic hairpin assembly-hybrid chain reaction (CHA-HCR). The electrochemical detection of silver on the electrode by linear sweep voltammetry (LSV) can be utilized to quantitatively measure miR-205-5p since the amount of silver deposited on the electrode is proportional to the target nucleic acid. The current response values exhibit strong linearity with the logarithm of miR-205-5p concentrations ranging from 0.1 pM to 10 µM, and the detection limit is 28 fM. A consistent trend was found in the results of the qRT-PCR and electrochemical biosensor techniques, which were employed to determine the total RNA recovered from cells, respectively. Moreover, the constructed sensor was used to assess miR-205-5p on various cell counts, and the outcomes demonstrated the excellent analytical efficiency of the proposed strategy. The recoveries ranged from 97.85% to 115.3% with RSDs of 2.251% to 4.869% in human serum samples. Our electrochemical biosensor for miR-205-5p detection exhibits good specificity, high sensitivity, repeatability, and stability. It is a potentially useful sensing platform for tumor diagnosis and tumor type identification in clinical settings.

Deciphering the SOX4/MAPK1 regulatory axis: a phosphoproteomic insight into IQGAP1 phosphorylation and pancreatic Cancer progression.

OBJECTIVE: This study aims to elucidate the functional role of IQGAP1 phosphorylation modification mediated by the SOX4/MAPK1 regulatory axis in developing pancreatic cancer through phosphoproteomics analysis. METHODS: Proteomics and phosphoproteomics data of pancreatic cancer were obtained from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. Differential analysis, kinase-substrate enrichment analysis (KSEA), and independent prognosis analysis were performed on these datasets. Subtype analysis of pancreatic cancer patients was conducted based on the expression of prognostic-related proteins, and the prognosis of different subtypes was evaluated through prognosis analysis. Differential analysis of proteins in different subtypes was performed to identify differential proteins in the high-risk subtype. Clinical correlation analysis was conducted based on the expression of prognostic-related proteins, pancreatic cancer typing results, and clinical characteristics in the pancreatic cancer proteomics dataset. Functional pathway enrichment analysis was performed using GSEA/GO/KEGG, and most module proteins correlated with pancreatic cancer were selected using WGCNA analysis. In cell experiments, pancreatic cancer cells were grouped, and the expression levels of SOX4, MAPK1, and the phosphorylation level of IQGAP1 were detected by RT-qPCR and Western blot experiments. The effect of SOX4 on MAPK1 promoter transcriptional activity was assessed using a dual-luciferase assay, and the enrichment of SOX4 on the MAPK1 promoter was examined using a ChIP assay. The proliferation, migration, and invasion functions of grouped pancreatic cancer cells were assessed using CCK-8, colony formation, and Transwell assays. In animal experiments, the impact of SOX4 on tumor growth and metastasis through the regulation of MAPK1-IQGAP1 phosphorylation modification was studied by constructing subcutaneous and orthotopic pancreatic cancer xenograft models, as well as a liver metastasis model in nude mice. RESULTS: Phosphoproteomics and proteomics data analysis revealed that the kinase MAPK1 may play an important role in pancreatic cancer progression by promoting IQGAP1 phosphorylation modification. Proteomics analysis classified pancreatic cancer patients into two subtypes, C1 and C2, where the high-risk C2 subtype was associated with poor prognosis, malignant tumor typing, and enriched tumor-related pathways. SOX4 may promote the occurrence of the high-risk C2 subtype of pancreatic cancer by regulating MAPK1-IQGAP1 phosphorylation modification. In vitro cell experiments confirmed that SOX4 promoted IQGAP1 phosphorylation modification by activating MAPK1 transcription while silencing SOX4 inhibited the proliferation, migration, and invasion of pancreatic cancer cells by reducing the phosphorylation level of MAPK1-IQGAP1. In vivo, animal experiments further confirmed that silencing SOX4 suppressed the growth and metastasis of pancreatic cancer by reducing the phosphorylation level of MAPK1-IQGAP1. CONCLUSION: The findings of this study suggest that SOX4 promotes the phosphorylation modification of IQGAP1 by activating MAPK1 transcription, thereby facilitating the growth and metastasis of pancreatic cancer.

Multiple RNA Rapid In Situ Imaging Based on Cas9 Code Key System.

Existing RNA in situ imaging strategies mostly utilize parallel repetitive nucleic acid self-assembly to achieve multiple analysis, with limitations of complicated systems and cumbersome steps. Here, a Cas9 code key system with key probe (KP) encoder and CRISPR/Cas9 signal exporter is developed. This system triggers T-protospacer adjacent motif (T-PAM structural transitions of multiple KP encoders to form coding products with uniform single-guide RNA (sgRNA) target sequences as tandem nodes. Only single sgRNA/Cas9 complex is required to cleave multiple coding products, enabling efficient "many-to-one" tandem signaling, and non-collateral cleavage activity-dependent automatic signaling output through active introduction of mismatched bases. Compared with conventional parallel multiple signaling analysis model, the proposed system greatly simplifies reaction process and enhances detection efficiency. Further, a rapid multiple RNA in situ imaging system is developed by combining the Cas9 code key system with a T-strand displacement amplification (T-SDA) signal amplifier. The constructed system is applied to tumor cells and clinicopathology slices, generating clear multi-mRNA imaging profiles in less than an hour with just one step. Therefore, this work provides reliable technical support for clinical tumor typing and molecular mechanism investigation.

Effectiveness and Safety of Different Postoperative Adjuvant Regimens in Patients with Low-Grade Gliomas: A Network Meta-Analysis.

OBJECTIVE: This study aimed to investigate the effectiveness and safety of various adjuvant regimens in patients with low-grade gliomas and to further explore the optimal adjuvant treatment for patients with low-grade gliomas and the differences in the efficacy of each treatment regimens in different tumor types. METHODS: A comprehensive search of the PubMed, Cochrane Library, Ovid, Embase, and Web of Science databases was conducted to screen randomized and nonrandomized controlled trials related to adjuvant therapy in patients with low-grade gliomas. The Cochrane quality assessment method and the Newcastle-Ottawa Scale were used to assess the quality of the included randomized and nonrandomized controlled trials, respectively. The data from previous studies were extracted using Excel and GetData Graph Digitizer 2.26 software, and network meta-analysis was performed using RevMan 5.3 and Stata 16.0 statistical software. RESULTS: The specific ranking of 5-year progression-free survival (5-year PFS) for each treatment regimen from the best to the worst in patients with low-grade gliomas was surgery (S) combined with procarbazine, lomustine, and vincristine (S + PCV); surgery combined with standard radiotherapy and PCV multidrug chemotherapy (S + RT + PCV); surgery combined with standard radiotherapy and temozolomide monotherapy (S + RT + TMZ); surgery combined with enhanced radiotherapy (S + H-RT); surgery combined with standard radiotherapy (S + RT); surgery combined with TMZ (S + TMZ); and S. The 5-year overall survival (OS) ranking was S + RT + TMZ, S + RT + PCV, surgery combined with enhanced radiotherapy and TMZ monotherapy (S + H-RT + TMZ), S + H-RT, S + RT, and S. The 2-year progression-free survival ranking was S + RT + TMZ, S + PCV, S + RT, S + RT + PCV, S + TMZ, S + H-RT, and S. The 2-year overall survival ranking was S + RT + TMZ, S + H-RT + TMZ, S + RT, S + RT + PCV, S + H-RT, and S. The incidence of adverse events (≥3) was ranked from highest to lowest as follows: S + RT + PCV, S + RT + TMZ, S + PCV, S + H-RT, S + TMZ, and S + RT. In the isocitrate dehydrogenase 1/2 mutation nonchromosome 1p and 19q chromosome whole arm codeletion (IDHmt/noncoder) group, the S + RT + PCV and S + H-RT regimens had better 5-year PFS and 5-year OS. In the isocitrate dehydrogenase 1/2 mutation and chromosome 1p and 19q chromosome whole arm codeletion (IDHmt/coder) group, the 5-year PFS of each treatment regimen ranked from the best to the worst was S + RT + TMZ, S + RT + PCV, S + H-RT, S + RT, S + TMZ, and S. The order of 5-year OS from the best to the worst was S + H-RT, S + RT + TMZ, S + RT + PCV, S + RT, and S. In the isocitrate dehydrogenase 1/2 wild-type (IDHwt) group, the S + H-RT and S + TMZ regimens had better 5-year PFS. CONCLUSIONS: This study revealed that both the S + RT + TMZ and S + RT + PCV regimens might be effective therapies for treating patients with low-grade gliomas. Among these, the S + RT + TMZ regimen seemed to be safer but might lead to tumor deterioration. In the IDHmt/coder type, the S + RT + TMZ scheme might have a significant advantage. In the IDHmt/noncoder type, the S + RT + PCV scheme might be more dominant, while in the IDHwt type, the S + H-RT and S + TMZ schemes also might be good treatment options.

Recommendations for immunocytochemistry in lung cancer typing: An update on a resource-efficient approach with large-scale comparative Bayesian analysis.

OBJECTIVES: The majority of lung cancer cases are of advanced stage and diagnosis is usually made using minimally invasive small biopsies and cytological specimens. The WHO 2015 classification recommends limiting immunocytochemistry (ICC) to lung cancer typing and molecular testing drives for personalised therapies. An algorithm using Bayes' theorem could be useful for defining antibody profiles. This study aims to assess the impact of different antibody profiles for cytological samples on the accuracy of lung cancer typing with a large-scale Bayesian analysis. METHODS: A retrospective examination of 3419 consecutive smears and/or cytospins diagnosed over 2011-2016 found 1960 primary lung cancer tumours: 972 adenocarcinomas (ADC), 256 squamous carcinomas (SQC), 268 neuroendocrine tumours (NET), and 464 non-small cell cancer-not otherwise specified (NSCC-NOS). The a priori and a posteriori probabilities, before and after ICC using antibodies singly or in combination, were calculated for different lung cancer types. RESULTS: TTF-1 or CK7 alone improved the a posteriori probabilities of correct cytological typing for ADC to 86.5% and 95.8%, respectively. For SQC, using p40 (∆Np63) or CK5/6 together with CK5/14 led to comparable results (78.3% and 90.3%). With synaptophysin or CD56 alone, improvements in a posteriori probabilities to 87.5 and 90.3% for the correct recognition of NET could be achieved. CONCLUSIONS: Based on morphological and clinical data, the use of two antibodies appears sufficient for reliable detection of the different lung cancer types. This applies to diagnoses that were finalised following ICC both on a clinical or cytological basis and on a histological basis.

Development of a Class Prediction Model to Discriminate Pancreatic Ductal Adenocarcinoma from Pancreatic Neuroendocrine Tumor by MALDI Mass Spectrometry Imaging.

PURPOSE: To define proteomic differences between pancreatic ductal adenocarcinoma (pDAC) and pancreatic neuroendocrine tumor (pNET) by matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI). EXPERIMENTAL DESIGN: Ninety-three pDAC and 126 pNET individual tissues are assembled in tissue microarrays and analyzed by MALDI MSI. The cohort is separated in a training (52 pDAC and 83 pNET) and validation set (41 pDAC and 43 pNET). Subsequently, a linear discriminant analysis (LDA) model based on 46 peptide ions is performed on the training set and evaluated on the validation cohort. Additionally, two liver metastases and a whole slide of pDAC are analyzed by the same LDA algorithm. RESULTS: Classification of pDAC and pNET by the LDA model is correct in 95% (39/41) and 100% (43/43) of patients in the validation cohort, respectively. The two liver metastases and the whole slide of pDAC are also correctly classified in agreement with the histopathological diagnosis. CONCLUSION AND CLINICAL RELEVANCE: In the present study, a large dataset of pDAC and pNET by MALDI MSI is investigated, a class prediction model that allowed separation of both entities with high accuracy is developed, and differential peptide peaks with potential diagnostic, prognostic, and predictive values are highlighted.

Targeted Feature Extraction in MALDI Mass Spectrometry Imaging to Discriminate Proteomic Profiles of Breast and Ovarian Cancer.

PURPOSE: To develop a mass spectrometry imaging (MSI) based workflow for extracting m/z values related to putative protein biomarkers and using these for reliable tumor classification. EXPERIMENTAL DESIGN: Given a list of putative breast and ovarian cancer biomarker proteins, a set of related m/z values are extracted from heterogeneous MSI datasets derived from formalin-fixed paraffin-embedded tissue material. Based on these features, a linear discriminant analysis classification model is trained to discriminate the two tumor types. RESULTS: It is shown that the discriminative power of classification models based on the extracted features is increased compared to the automatic training approach, especially when classifiers are applied to spectral data acquired under different conditions (instrument, preparation, laboratory). CONCLUSIONS AND CLINICAL RELEVANCE: Robust classification models not confounded by technical variation between MSI measurements are obtained. This supports the assumption that the classification of the respective tumor types is based on biological rather than technical differences, and that the selected features are related to the proteomic profiles of the tumor types under consideration.

Clinical neuropathology of brain tumors.

Brain tumor typing is a major task in the daily practice of clinical neuropathologists. For more than 100 years, brain tumors have been classified on the basis of a histogenetic concept, with the definition of more than 120 brain tumor entities over time. In the past decades, biomedical research on brain tumors has led to the identification of clinically meaningful diagnostic, prognostic, and predictive molecular markers. Taking this progress into account, the 2016 update of the World Health Organization classification of tumors of the central nervous system has incorporated for the first time molecular markers for definition of brain tumor entities. This development has resulted in integrated diagnostics on the basis of histologic and molecular characteristics. This chapter summarizes essential features of brain tumors in the light of integrated diagnostics. To provide a comprehensive view on the individual tumor entities, we included crucial epidemiologic, clinical, and neuroradiologic aspects as well. In addition we illustrate neuroimaging and histologic characteristics of the various tumor types. In this way we aim to provide concise up-to-date insight into the nature and classification of brain tumors.

Elastic and inelastic light scattering spectroscopy and its possible use for label-free brain tumor typing.

This paper presents an approach for label-free brain tumor tissue typing. For this application, our dual modality microspectroscopy system combines inelastic Raman scattering spectroscopy and Mie elastic light scattering spectroscopy. The system enables marker-free biomedical diagnostics and records both the chemical and morphologic changes of tissues on a cellular and subcellular level. The system setup is described and the suitability for measuring morphologic features is investigated. Graphical Abstract Bimodal approach for label-free brain tumor typing. Elastic and inelastic light scattering spectra are collected laterally resolved in one measurement setup. The spectra are investigated by multivariate data analysis for assigning the tissues to specific WHO grades according to their malignancy.

A new classification method for MALDI imaging mass spectrometry data acquired on formalin-fixed paraffin-embedded tissue samples.

Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) shows a high potential for applications in histopathological diagnosis, and in particular for supporting tumor typing and subtyping. The development of such applications requires the extraction of spectral fingerprints that are relevant for the given tissue and the identification of biomarkers associated with these spectral patterns. We propose a novel data analysis method based on the extraction of characteristic spectral patterns (CSPs) that allow automated generation of classification models for spectral data. Formalin-fixed paraffin embedded (FFPE) tissue samples from N=445 patients assembled on 12 tissue microarrays were analyzed. The method was applied to discriminate primary lung and pancreatic cancer, as well as adenocarcinoma and squamous cell carcinoma of the lung. A classification accuracy of 100% and 82.8%, resp., could be achieved on core level, assessed by cross-validation. The method outperformed the more conventional classification method based on the extraction of individual m/z values in the first application, while achieving a comparable accuracy in the second. LC-MS/MS peptide identification demonstrated that the spectral features present in selected CSPs correspond to peptides relevant for the respective classification. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.

Typing of colon and lung adenocarcinoma by high throughput imaging mass spectrometry.

In advanced tumor stages, diagnosis is frequently made from metastatic tumor tissue. In some cases, the identification of the tumor of origin may be difficult by histology alone. In this setting, immunohistochemical and molecular biological methods are often required. In a subset of tumors definite diagnosis cannot be achieved. Thus, additional new diagnostic methods are required for precise tumor subtyping. Mass spectrometric methods are of special interest for the discrimination of different tumor types. We investigated whether it is possible to discern adenocarcinomas of colon and lung using high-throughput imaging mass spectrometry on formalin-fixed paraffin-embedded tissue microarrays. 101 primary adenocarcinoma of the colon and 91 primary adenocarcinoma of the lung were used to train a Linear Discriminant Analysis model. Results were validated on an independent set of 116 colonic and 75 lung adenocarcinomas. In the validation cohort 109 of 116 patients with colonic and 67 of 75 patients with lung adenocarcinomas were correctly classified. The ability to define proteomic profiles capable to discern different tumor types promises a valuable tool in cancer diagnostics and might complement current approaches. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.

[Grading of tumors in the tubular digestive tract : Esophagus, stomach, colon and rectum]. / Grading der Tumoren des tubulären Verdauungssystems : Ösophagus, Magen, Kolon und Rektum.

Grading of tumors located in the tubular digestive tract is an integral component of pathology assessment reports but is subordinate to the histological typing of tumors with respect to the prognostic significance. Tumor grading has not been shown to be an independent prognostic marker for most tumor entities in the gastrointestinal tract; however, it may be relevant for further routine treatment decision making in early Union Internationale Contre le Cancer (UICC) stage cancers in which the prognosis for patients is less dominated by advanced tumor spread. Owing to the more favorable prognosis of microsatellite instability in colorectal cancer, the World Health Organization (WHO) has recommended that poorly differentiated tumors should be tested and graded as low grade (G1/G2) when microsatellite instability is detected. This recommendation has been integrated into the German S3 guidelines for colorectal cancers. Accordingly, microsatellite instability testing for grading purposes should become routine practice.

Imaging mass spectrometry to discriminate breast from pancreatic cancer metastasis in formalin-fixed paraffin-embedded tissues.

Diagnosis of the origin of metastasis is mandatory for adequate therapy. In the past, classification of tumors was based on histology (morphological expression of a complex protein pattern), while supportive immunohistochemical investigation relied only on few "tumor specific" proteins. At present, histopathological diagnosis is based on clinical information, morphology, immunohistochemistry, and may include molecular methods. This process is complex, expensive, requires an experienced pathologist and may be time consuming. Currently, proteomic methods have been introduced in various clinical disciplines. MALDI imaging MS combines detection of numerous proteins with morphological features, and seems to be the ideal tool for objective and fast histopathological tumor classification. To study a special tumor type and to identify predictive patterns that could discriminate metastatic breast from pancreatic carcinoma MALDI imaging MS was applied to multitissue paraffin blocks. A statistical classification model was created using a training set of primary carcinoma biopsies. This model was validated on two testing sets of different breast and pancreatic carcinoma specimens. We could discern breast from pancreatic primary tumors with an overall accuracy of 83.38%, a sensitivity of 85.95% and a specificity of 76.96%. Furthermore, breast and pancreatic liver metastases were tested and classified correctly.