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Journal articles on the topic 'Signature score'

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

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1

Hummel, Manuela, Klaus H. Metzeler, Christian Buske, Stefan K. Bohlander, and Ulrich Mansmann. "Association between a Prognostic Gene Signature and Functional Gene Sets." Bioinformatics and Biology Insights 2 (January 2008): BBI.S1018. http://dx.doi.org/10.4137/bbi.s1018.

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Background The development of expression-based gene signatures for predicting prognosis or class membership is a popular and challenging task. Besides their stringent validation, signatures need a functional interpretation and must be placed in a biological context. Popular tools such as Gene Set Enrichment have drawbacks because they are restricted to annotated genes and are unable to capture the information hidden in the signature's non-annotated genes. Methodology We propose concepts to relate a signature with functional gene sets like pathways or Gene Ontology categories. The connection be

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Berglund, Anders E., Eric A. Welsh, and Steven A. Eschrich. "Characteristics and Validation Techniques for PCA-Based Gene-Expression Signatures." International Journal of Genomics 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/2354564.

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Background. Many gene-expression signatures exist for describing the biological state of profiled tumors. Principal Component Analysis (PCA) can be used to summarize a gene signature into a single score. Our hypothesis is that gene signatures can be validated when applied to new datasets, using inherent properties of PCA.Results. This validation is based on four key concepts. Coherence: elements of a gene signature should be correlated beyond chance. Uniqueness: the general direction of the data being examined can drive most of the observed signal. Robustness: if a gene signature is designed t

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Putz-Leszczyńska, Joanna. "Signature verification: A comprehensive study of the hidden signature method." International Journal of Applied Mathematics and Computer Science 25, no. 3 (2015): 659–74. http://dx.doi.org/10.1515/amcs-2015-0048.

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Abstract Many handwritten signature verification algorithms have been developed in order to distinguish between genuine signatures and forgeries. An important group of these methods is based on dynamic time warping (DTW). Traditional use of DTW for signature verification consists in forming a misalignment score between the verified signature and a set of template signatures. The right selection of template signatures has a big impact on that verification. In this article, we describe our proposition for replacing the template signatures with the hidden signature-an artificial signature which i

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Fan, Chunni, Jianshi Du, and Ning Liu. "Identification of a Transcription Factor Signature That Can Predict Breast Cancer Survival." Computational and Mathematical Methods in Medicine 2021 (February 19, 2021): 1–15. http://dx.doi.org/10.1155/2021/2649123.

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Background. The expression pattern of transcription factors (TFs) can be used to develop potential prognostic biomarkers for cancer. In this study, we aimed to identify and validate a TF signature for predicting disease-free survival (DFS) of breast cancer (BRCA) patients. Methods. Lasso and the Cox regression analyses were applied to construct a TF signature based on a gene expression dataset from TCGA. The prognosis value of the TF signature was investigated in the TCGA database, and its reliability was further validated in 3 independent datasets from Gene Expression Omnibus (GEO). The progn

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Srilekha, Nalla. "Signature Recognition and Verification Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32231.

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This project focuses on the development and implementation of a robust signature recognition and verification system leveraging machine learning techniques. Handwritten signatures serve as essential personal identifiers in numerous applications, such as financial transactions, legal documents, and access control. Traditional methods of signature verification often lack efficiency and accuracy, prompting the need for automated and intelligent systems. The proposed project aims to address this challenge by employing state-of-the-art machine learning algorithms for signature analysis. The project

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Motzer, Robert J., Camillo Porta, Masatoshi Eto, et al. "Biomarker analyses in patients with advanced renal cell carcinoma (aRCC) from the phase 3 CLEAR trial." Journal of Clinical Oncology 42, no. 16_suppl (2024): 4504. http://dx.doi.org/10.1200/jco.2024.42.16_suppl.4504.

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4504 Background: In the primary analysis of CLEAR, lenvatinib + pembrolizumab (L+P) significantly improved efficacy vs sunitinib (S) in treatment-naïve patients with aRCC (Motzer 2021). Results were confirmed at the final prespecified OS analysis (Motzer 2024). We report biomarker analyses from CLEAR. Methods: PD-L1 IHC 22C3 pharmDx and NGS assays (ImmunoID NeXT platform: WES and RNA-Seq) were performed on archival tumor specimens. To identify somatic alterations including mutations and copy-number variations, paired PBMC samples were used as reference. For RNA-Seq/IHC-derived analyses, a cont

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Braune, Eike-Benjamin, Felix Geist, Xiaojia Tang, et al. "Abstract B013: Identification of a Notch transcriptomic signature for breast cancer." Molecular Cancer Therapeutics 22, no. 12_Supplement (2023): B013. http://dx.doi.org/10.1158/1535-7163.targ-23-b013.

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Abstract Background: Dysregulated Notch signalling contributes to breast cancer development and progression, but validated tools to measure the level of Notch signalling in breast cancer subtypes and in response to systemic therapy are largely lacking. A transcriptomic signature of Notch signalling would thus be warranted, and in this report, we have established such a classifier. Methods: To generate the signature, we first identified Notch-regulated genes from six basal-like breast cancer cell lines subjected to elevated and reduced Notch signalling by culturing on immobilized Notch ligand J

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Vaijayanthimala, J., and T. Padma. "Synthesis Score Level Fusion Based Multifarious Classifier for Multi-Biometrics Applications." Journal of Medical Imaging and Health Informatics 9, no. 8 (2019): 1673–80. http://dx.doi.org/10.1166/jmihi.2019.2762.

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In this paper, we are presenting a face and signature recognition method from a large dataset with the different pose and multiple features. Initially, Face and corresponding signature are detected from devices for further pre-processing. Face recognition is the first stage of a system then the signature verification will be done. The proposed Legion feature based verification method will be developed using four important steps like, (i) feature extraction from face and data glove signals using feature Extraction. The various Features like Local binary pattern, shape and geometrical features o

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Wang, Kang, Yajing Zhu, Ioannis Zerdes, et al. "Abstract PO2-07-06: Multimodal learning predictor of HER2-positive breast cancer therapy response in the randomized PREDIX HER2 trial." Cancer Research 84, no. 9_Supplement (2024): PO2–07–06—PO2–07–06. http://dx.doi.org/10.1158/1538-7445.sabcs23-po2-07-06.

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Abstract Background: The PREDIX HER2 trial, compared six courses of docetaxel, trastuzumab, and pertuzumab (DTP) vs. trastuzumab emtansine (T-DM1) as neoadjuvant treatment for HER2-positive breast cancer (BC). Similar rates of pathologic complete response (pCR) were seen. Methods: Clinicopathological, shallow whole-genome sequencing (CUTseq, n=176), whole exome sequencing (WES, n=192), and RNA-sequencing (RNA-seq, n=187) data were generated using fresh-frozen baseline core biopsies. Potential tumor intrinsic resistance factors and microenvironment components were quantified by multi-omics anal

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Smith, Michael Alexander, Chia-Chien Chiang, Dominic Sinibaldi, et al. "Using the Circulating Proteome to Assess Type I Interferon Activity in Systemic Lupus Erythematosus." Journal of Immunology 198, no. 1_Supplement (2017): 210.1. http://dx.doi.org/10.4049/jimmunol.198.supp.210.1.

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Abstract Type I interferon (IFN) signaling drives pathology in systemic lupus erythematosus (SLE) and can be tracked via IFN-inducible transcripts present in whole blood as described by several IFN gene signatures. As SLE is a complex disease affecting diverse organ systems, we examined whether measurement of circulating proteins, which can infiltrate the bloodstream from afflicted tissues, might also offer insight into global IFN activity. The presence of anti-DNA autoantibodies in patient serum has prevented effective use of SOMAmers for the evaluation of circulating proteins in SLE. Here, w

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Ma, Yibo, Changgui Tong, Mingjun Xu, Hongtao He, and Changjian Chen. "Bioinformatics Analysis Reveals an Association between Autophagy, Prognosis, Tumor Microenvironment, and Immunotherapy in Osteosarcoma." Journal of Oncology 2022 (July 14, 2022): 1–15. http://dx.doi.org/10.1155/2022/4220331.

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Autophagy is a catabolic pathway involved in the regulation of bone homeostasis. We explore clinical correlation of autophagy-related key molecules to establish risk signature for predicting the prognosis, tumor microenvironment (TME), and immunotherapy response of osteosarcoma. Single cell RNA sequencing data from GSE162454 dataset distinguished malignant cells from normal cells in osteosarcoma. Autophagy-related genes (ARGs) were extracted from the established risk signature of the Molecular Signatures Database of Gene Set Enrichment Analysis (GSEA) by univariate Cox and least absolute shrin

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Chen, Shuzhao, Limei Zhang, Haocheng Lin, Yang Liang, and Yun Wang. "Functional Gene Expression Signatures from On-Treatment Tumor Specimens Predict Anti-PD1 Blockade Response in Metastatic Melanoma." Biomolecules 13, no. 1 (2022): 58. http://dx.doi.org/10.3390/biom13010058.

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Functional gene expression signatures (FGES) from pretreatment biopsy samples have been used to predict the responses of metastatic melanoma to immune checkpoint blockade (ICB) therapies. However, there are no predictive FGE signatures from patients receiving treatment. Here, using the Elastic Net Regression (ENLR) algorithm, we analyzed transcriptomic and matching clinical data from a dataset of patients with metastatic melanoma treated with ICB therapies and produced an FGE signature for pretreatment (FGES-PRE) and on-treatment (FGES-ON). Both the FGES-PRE and FGES-ON signatures are validate

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Penev, Petar I., Claudia Alvarez-Carreño, Eric Smith, Anton S. Petrov, and Loren Dean Williams. "TwinCons: Conservation score for uncovering deep sequence similarity and divergence." PLOS Computational Biology 17, no. 10 (2021): e1009541. http://dx.doi.org/10.1371/journal.pcbi.1009541.

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We have developed the program TwinCons, to detect noisy signals of deep ancestry of proteins or nucleic acids. As input, the program uses a composite alignment containing pre-defined groups, and mathematically determines a ‘cost’ of transforming one group to the other at each position of the alignment. The output distinguishes conserved, variable and signature positions. A signature is conserved within groups but differs between groups. The method automatically detects continuous characteristic stretches (segments) within alignments. TwinCons provides a convenient representation of conserved,

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Ballot, Elise, Sylvain Ladoire, Bertrand Routy, Caroline Truntzer, and François Ghiringhelli. "Tumor Infiltrating Lymphocytes Signature as a New Pan-Cancer Predictive Biomarker of Anti PD-1/PD-L1 Efficacy." Cancers 12, no. 9 (2020): 2418. http://dx.doi.org/10.3390/cancers12092418.

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Tumor immune infiltrates are associated with tumor prognosis in many cancer types. However, their capacity to predict the efficacy of checkpoint inhibitors is poorly documented. We generate three signatures that evaluate in different ways these infiltrates: lymphoid- and myeloid-alone signatures, and a combined signature of both named the TIL (tumor-infiltrating lymphocyte) transcriptomic signature. We evaluate these signatures in The Cancer Genome Atlas Program (TCGA) Pan-Cancer cohort and four cohorts comprising patients with melanoma, lung, and head and neck cancer treated with anti-PD-1 or

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Leghari, Mehwish, Asghar ali Chandio, Muhammad Ali Soomro, Shah Zaman Nizamani, and Muhammad Hanif Soomro. "A Comparative Analysis of Machine Learning Algorithms for Online Signature Recognition." VFAST Transactions on Software Engineering 12, no. 2 (2024): 231–40. http://dx.doi.org/10.21015/vtse.v12i2.1845.

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Biometrics recognition plays a vital role in modern human recognition and verification systems. An extensive latest research by the research community has rendered the field of biometrics inevitable for real-life applications. This research study focuses on online signature recognition. The research study is performed to identify if an online signature is genuine or forged. A novel online signature dataset, based on 1000 online signatures, has been collected from 200 participants, wherein every participant provided 5 instances of the online signature. An Android-based mobile application was de

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Zhang, Jiaojiao, Blessed Kondowe, Hui Zhang, et al. "Identification of prognostic indicator based on hypoxia-related lncRNAs analysis in lung adenocarcinoma." Malawi Medical Journal 36, no. 3 (2024): 170–78. https://doi.org/10.4314/mmj.v36i3.3.

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Introduction There were no systematic studies about hypoxia-related long noncoding RNAs (lncRNAs) signatures to predict the survival of patients with lung adenocarcinoma (LUAD). Setting up matching hypoxia-related lncRNA signatures was necessary.ObjectiveThis study aimed to establish hypoxia-related lncRNAs signatures and to seek new biomarkers to predict the prognosis of the patients with lung adenocarcinoma.MethodologyThe Cancer Genome Atlas (TCGA) database provided the expression profiles of lncRNAs that includes 535 lung adenocarcinoma samples. The coexpression network of lncRNAs and hypox

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Zhou, Yiwen, Jianbin Zheng, Huacheng Hu, and Yizhen Wang. "Handwritten Signature Verification Method Based on Improved Combined Features." Applied Sciences 11, no. 13 (2021): 5867. http://dx.doi.org/10.3390/app11135867.

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As a behavior feature, handwritten signatures are widely used in financial and administrative institutions. The appearance of forged signatures will cause great property losses to customers. This paper proposes a handwritten signature verification method based on improved combined features. According to advanced smart pen technology, when writing a signature, offline images and online data of the signature can be obtained in real time. It is the first time to realize the combination of offline and online. We extract the static and dynamic features of the signature and verify them with support

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G V, Giridhar, and Vijayalaxmi S D. "An Innovative Approaches to Handwritten Signature Verification using CNN." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 07 (2025): 1–9. https://doi.org/10.55041/ijsrem51326.

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Handwritten signatures serve as a well known way for identity verification, delivering distinctive biometric feature for personal identification. The complexity and variety of signatures offer substantial obstacles in attaining reliable verification. This work proposes a unique technique to handwritten signature substantiation using CNNs. The purpose is to harness authority of vast knowledge to boost accuracy & reliability of signature verification system. CNNs are used due to their amazing capabilities in image processing & feature extraction. Proposed system adopts a Siamese network

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Chen, J. L., J. Li, W. M. Stadler, and Y. A. Lussier. "Use of protein-network modeling of prostate cancer gene signatures to investigate essential pathways in disease recurrence." Journal of Clinical Oncology 29, no. 7_suppl (2011): 46. http://dx.doi.org/10.1200/jco.2011.29.7_suppl.46.

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46 Background: Numerous molecular pathways have been implicated in aggressive prostate cancer. However, it has been difficult to gain an overview of the dominant pathways involved. By analyzing multiple prostate cancer gene signatures of poor prognosis through a protein interaction network, we are able to in an unbiased manner prioritize shared key biological mechanisms. Methods: We evaluate prostate gene signatures through a previously published Single Protein Analysis of Network (SPAN) methodology to develop a prostate cancer network signature. We assess this signature using a Gene Ontology

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Sangdiah, Nana Suarna, Irfan Ali, and Dendy Indriya Efendi. "Authenticity Accuracy Improvement Through the Analysis of Signature Ownership Using Convolutional Neural Network Algorithm." Journal of Artificial Intelligence and Engineering Applications (JAIEA) 4, no. 2 (2025): 1289–93. https://doi.org/10.59934/jaiea.v4i2.900.

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This research aims to improve the accuracy of signature authenticity classification using a Convolutional Neural Network (CNN) model, implemented in a web-based application using the Flask framework. In the digital era, signature authentication has become a crucial component in maintaining data security and transaction validity. However, the classification of genuine and forged signatures presents its own challenges due to the unique variations in patterns and styles of each individual. Using a public dataset from Kaggle consisting of 1,084 signature images (620 forged and 464 genuine), the CN

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Zhou, Weige, Shijing Zhang, Hui-biao Li, et al. "Development of Prognostic Indicator Based on Autophagy-Related lncRNA Analysis in Colon Adenocarcinoma." BioMed Research International 2020 (September 3, 2020): 1–14. http://dx.doi.org/10.1155/2020/9807918.

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There were no systematic researches about autophagy-related long noncoding RNA (lncRNA) signatures to predict the survival of patients with colon adenocarcinoma. It was necessary to set up corresponding autophagy-related lncRNA signatures. The expression profiles of lncRNAs which contained 480 colon adenocarcinoma samples were obtained from The Cancer Genome Atlas (TCGA) database. The coexpression network of lncRNAs and autophagy-related genes was utilized to select autophagy-related lncRNAs. The lncRNAs were further screened using univariate Cox regression. In addition, Lasso regression and m

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Guo, Yuheng, and Hiroyuki Sato. "Smartwatch In-Air Signature Time Sequence Three-Dimensional Static Restoration Classification Based on Multiple Convolutional Neural Networks." Applied Sciences 13, no. 6 (2023): 3958. http://dx.doi.org/10.3390/app13063958.

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In-air signatures are promising applications that have been investigated extensively in the past decades; an in-air signature involves gathering datasets through portable devices, such as smartwatches. During the signing process, individuals wear smartwatches on their wrists and sign their names in the air. The dataset we used in this study collected in-air signatures from 22 participants, resulting in a total of 440 smartwatch in-air signature signals. The dynamic time warping (DTW) algorithm was applied to verify the usability of the dataset. This paper analyzes and compares the performances

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Bali, Manik. "Signature Verification System Using Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30155.

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The proposed system employs a convolutional neural network (CNN) architecture for signature feature extraction and classification. Furthermore, the system integrates preprocessing modules for signature image normalization, noise reduction, and feature extraction to enhance the robustness and accuracy of the verification process. Extensive experimentation and evaluation are conducted on benchmark datasets, including the widely used Tobacco 800 dataset and Kaggle dataset, to assess the performance of the proposed system in terms of accuracy, precision, recall, and score metrics. The results demo

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Rydenfelt, Mattias, Bertram Klinger, Martina Klünemann, and Nils Blüthgen. "SPEED2: inferring upstream pathway activity from differential gene expression." Nucleic Acids Research 48, W1 (2020): W307—W312. http://dx.doi.org/10.1093/nar/gkaa236.

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Abstract Extracting signalling pathway activities from transcriptome data is important to infer mechanistic origins of transcriptomic dysregulation, for example in disease. A popular method to do so is by enrichment analysis of signature genes in e.g. differentially regulated genes. Previously, we derived signatures for signalling pathways by integrating public perturbation transcriptome data and generated a signature database called SPEED (Signalling Pathway Enrichment using Experimental Datasets), for which we here present a substantial upgrade as SPEED2. This web server hosts consensus sign

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Tian, Yuan, Jingnan Wang, Qing Wen, et al. "The Significance of Tumor Microenvironment Score for Breast Cancer Patients." BioMed Research International 2022 (April 28, 2022): 1–27. http://dx.doi.org/10.1155/2022/5673810.

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Purpose. This study was designed to clarify the prognostic value of tumor microenvironment score and abnormal genomic alterations in TME for breast cancer patients. Method. The TCGA-BRCA data were downloaded from TCGA and analyzed with R software. The results from analyses were further validated using the dataset from GSE96058, GSE124647, and GSE25066. Results. After analyzing the TCGA data and verifying it with the GEO data, we developed a TMEscore model based on the TME infiltration pattern and validated it in 3273 breast cancer patients. The results suggested that our TMEscore model has hig

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Lin, Emily Pei-Ying, Tzu-Hung Hsiao, Jo-yang Lu, et al. "Translating Gene Signatures Into a Pathologic Feature: Tumor Necrosis Predicts Disease Relapse in Operable and Stage I Lung Adenocarcinoma." JCO Precision Oncology, no. 2 (November 2018): 1–13. http://dx.doi.org/10.1200/po.18.00043.

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Purpose The high 5-year disease relapse rate in patients with stage I lung adenocarcinoma indicates the need for additional risk stratification parameters. This study assessed whether gene signatures translate into a pathologic feature for better disease stratification. Materials and Methods The mutual interdependence and risk stratification power of three gene signatures, cell cycle, hypoxia, and mammalian target of rapamycin (mTOR), were investigated in nine cohorts of patients with lung adenocarcinoma and five cohorts of patients with lung squamous cell carcinoma. The translation from gene

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Tschodu, Dimitrij, Bernhard Ulm, Klaus Bendrat, et al. "Comparative analysis of molecular signatures reveals a hybrid approach in breast cancer: Combining the Nottingham Prognostic Index with gene expressions into a hybrid signature." PLOS ONE 17, no. 2 (2022): e0261035. http://dx.doi.org/10.1371/journal.pone.0261035.

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The diagnosis of breast cancer—including determination of prognosis and prediction—has been traditionally based on clinical and pathological characteristics such as tumor size, nodal status, and tumor grade. The decision-making process has been expanded by the recent introduction of molecular signatures. These signatures, however, have not reached the highest levels of evidence thus far. Yet they have been brought to clinical practice based on statistical significance in prospective as well as retrospective studies. Intriguingly, it has also been reported that most random sets of genes are sig

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Singh, Sandeep, and Sandeep Kaur. "ONLINE SIGNATURE VERIFICATION SYSTEM ON MOBILE DEVICES FOR EFFECTIVE AND SECURE BIOMETRIC." EPH - International Journal of Science And Engineering 3, no. 1 (2017): 20–25. http://dx.doi.org/10.53555/eijse.v3i1.43.

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Handwritten signature is that the most generally accepted biometric to biometric identification. The projected on-line written signature verification system consists principally of 3 phases: Signal preprocessing, feature extraction, and have matching. Steps for confirming on-line written signature during this system begin with extracting dynamic knowledge (x and y positions) of points that forming the signature. Pen-movement angles and speed square measure then derived from pen position knowledge. To scale back variations in pen-position and pen-movement angles spatial property, knowledge is n

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Bever, Alaina M., Dong Hang, Amit D. Joshi, et al. "Abstract 3006: Metabolomic signatures of metabolic disturbance and inflammation in relation to colorectal cancer risk." Cancer Research 83, no. 7_Supplement (2023): 3006. http://dx.doi.org/10.1158/1538-7445.am2023-3006.

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Abstract Background: Metabolic disturbance and inflammation may explain observed associations between higher body mass index (BMI) and increased risk of colorectal cancer (CRC); however, the underlying mechanisms are not fully understood. Objectives: We characterized individual plasma metabolites and metabolomic signatures of metabolic disturbance and inflammation and evaluated their association with prospective CRC risk within the Nurses’ Health Study and the Health Professionals Follow-up Study. Methods: Among 686 colorectal cancer cases and 686 age-matched controls, we used reduced rank reg

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Saputra, Muhammad Azi, and Ida Nurhaida. "Signature Originality Verification Using A Deep Learning Approach." Electronic Journal of Education, Social Economics and Technology 5, no. 1 (2024): 19–29. https://doi.org/10.33122/ejeset.v5i1.310.

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The rapid advancement of digital technology has heightened the need for reliable methods to verify signature authenticity, a critical aspect of document and transaction security. This study uses a deep learning approach to develop a mobile application to verify the originality of paper and digital media signatures. The dataset comprises 1,060 signature images, including authentic and forged categories for both media types. The system employs the EfficientNetV2M model, trained with augmented data, to enhance robustness. Model evaluation demonstrates strong performance with an accuracy of 82.07%

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Lavery, Anita, Leanne Stevenson, Damian McManus, et al. "Translational analysis of esophageal adenocarcinoma (EAC) patients treated with oxaliplatin and capecitabine (Xelox) +/- the dual ErbB inhibitor AZD8931 in the DEBIOC study." Journal of Clinical Oncology 38, no. 15_suppl (2020): 4539. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.4539.

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4539 Background: The Dual Erb B Inhibition in Oesophago-gastric Cancer (DEBIOC) trial reported an acceptable safety profile for neoadjuvant Xelox +/- AZD8931 but limited efficacy. We utilized EAC patient samples from DEBIOC to evaluate the impact of neoadjuvant Xelox +/-AZD8931 on biological pathways using a unique software driven solution. Methods: 24 pre-treatment FFPE EAC biopsies and 17 matched surgical resection specimens were transcriptionally profiled using the Almac Diagnostics Xcel Array. Gene expression data was analyzed using the Almac claraT total mRNA report V3.0.0, reporting on 9

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A, Jun, Baotong Zhang, Zhiqian Zhang, Hailiang Hu, and Jin-Tang Dong. "Novel Gene Signatures Predictive of Patient Recurrence-Free Survival and Castration Resistance in Prostate Cancer." Cancers 13, no. 4 (2021): 917. http://dx.doi.org/10.3390/cancers13040917.

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Molecular signatures predictive of recurrence-free survival (RFS) and castration resistance are critical for treatment decision-making in prostate cancer (PCa), but the robustness of current signatures is limited. Here, we applied the Robust Rank Aggregation (RRA) method to PCa transcriptome profiles and identified 287 genes differentially expressed between localized castration-resistant PCa (CRPC) and hormone-sensitive PCa (HSPC). Least absolute shrinkage and selection operator (LASSO) and stepwise Cox regression analyses of the 287 genes developed a 6-gene signature predictive of RFS in PCa.

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Liu, Han-Xuan, Jie Feng, Jing-Jing Jiang, et al. "Integrated single-cell and bulk RNA sequencing revealed an epigenetic signature predicts prognosis and tumor microenvironment colorectal cancer heterogeneity." World Journal of Gastrointestinal Oncology 16, no. 7 (2024): 3032–54. http://dx.doi.org/10.4251/wjgo.v16.i7.3032.

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BACKGROUND Colorectal cancer (CRC) prognosis prediction is currently a major challenge. Epigenetic regulation has been widely reported for its role in cancer development. AIM To construct a robust prognostic signature, we used developed and validated across datasets. METHODS After constructing the signature, the prognostic value of the signature was evaluated in the TCGA cohort and six independent datasets (GSE17526, GSE17537, GSE33113, GSE37892, GSE39048 and GSE39582). The clinical, genomic and transcriptomic features related to the signature were identified. The correlations of the signature

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Corino, Valentina D. A., Marco Bologna, Giuseppina Calareso, et al. "Refining Tumor Treatment in Sinonasal Cancer Using Delta Radiomics of Multi-Parametric MRI after the First Cycle of Induction Chemotherapy." Journal of Imaging 8, no. 2 (2022): 46. http://dx.doi.org/10.3390/jimaging8020046.

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Background: Response to induction chemotherapy (IC) has been predicted in patients with sinonasal cancer using early delta radiomics obtained from T1- and T2-weighted images and apparent diffusion coefficient (ADC) maps, comparing results with early radiological evaluation by RECIST. Methods: Fifty patients were included in the study. For each image (at baseline and after the first IC cycle), 536 radiomic features were extracted as follows: semi-supervised principal component analysis components, explaining 97% of the variance, were used together with a support vector machine (SVM) to develop

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Bai, Shuang, Ying-Bin Yan, Wei Chen, et al. "Bioinformatic Analysis Reveals an Immune/Inflammatory-Related Risk Signature for Oral Cavity Squamous Cell Carcinoma." Journal of Oncology 2019 (December 13, 2019): 1–10. http://dx.doi.org/10.1155/2019/3865279.

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High-throughput gene expression profiling has recently emerged as a promising technique that provides insight into cancer subtype classification and improved prediction of prognoses. Immune/inflammatory-related mRNAs may potentially enrich genes to allow researchers to better illustrate cancer microenvironments. Oral cavity squamous cell carcinoma (OC-SCC) exhibits high morbidity and poor prognosis compared to that of other types of head and neck squamous cell carcinoma (HNSCC), and these differences may be partially due to differences within the tumor microenvironments. Based on this, we desi

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Li, Chen, Jingyong Xu, Yuan Liu, et al. "Kupffer Phase Radiomics Signature in Sonazoid-Enhanced Ultrasound is an Independent and Effective Predictor of the Pathologic Grade of Hepatocellular Carcinoma." Journal of Oncology 2022 (June 27, 2022): 1–7. http://dx.doi.org/10.1155/2022/6123242.

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We conduct this study to investigate the value of Kupffer phase radiomics signature of Sonazoid-enhanced ultrasound images (SEUS) for the preoperative prediction of hepatocellular carcinoma (HCC) grade. From November 2019 to October 2021, 68 pathologically confirmed HCC nodules from 54 patients were included. Quantitative radiomic features were extracted from grayscale images and arterial and Kupffer phases of SEUS of HCC lesions. Univariate logistic regression and the maximum relevance minimum redundancy (MRMR) method were applied to select radiomic features best corresponding to pathological

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Liu, Jun, Jianjun Lu, and Wenli Li. "A Comprehensive Prognostic and Immunological Analysis of a New Three-Gene Signature in Hepatocellular Carcinoma." Stem Cells International 2021 (June 2, 2021): 1–25. http://dx.doi.org/10.1155/2021/5546032.

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There are few reports on the role of genes associated with the mRNA expression-based stemness index (mRNAsi) in the prognosis and immune regulation of hepatocellular carcinoma (HCC). This study is aimed at analyzing the expression profile and prognostic significance of a new mRNAsi-based three-gene signature in HCC. This three-gene signature was identified by analyzing mRNAsi data from the Cancer Genome Atlas (TCGA) HCC dataset. The prognostic value of the risk score based on the three-gene signature was evaluated by Cox regression and Kaplan-Meier analysis and then verified in the Internation

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Found, Bryan, Doug Rogers, and Allan Herkt. "The skill of a group of forensic document examiners in expressing handwriting and signature authorship and production process opinions." Journal of Forensic Document Examination 29 (December 31, 2019): 73–82. http://dx.doi.org/10.31974/jfde29-73-82.

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Between March 1998 and June 2001, the six members of the New Zealand Police Document Examination Section completed six blind forensic handwriting and signature trials where the identity of the questioned writings were known by the experimenters but not by the document examiners. A total of 7494 opinions were expressed by the examiner group regarding the authorship of questioned handwriting and signature samples. Of these opinions, 2700 were correct, 11 were erroneous and 4783 were inconclusive. This translates into an overall raw error score of 0.1% of opinions, and a ‘called error’ score (one

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Li, Nan, Kai Yu, Zhong Lin, and Dingyuan Zeng. "Identifying a cervical cancer survival signature based on mRNA expression and genome-wide copy number variations." Experimental Biology and Medicine 247, no. 3 (2021): 207–20. http://dx.doi.org/10.1177/15353702211053580.

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Cervical cancer mortality is the second highest in gynecological cancers. This study developed a new model based on copy number variation data and mRNA data for overall survival prediction of cervical cancer. Differentially expressed genes from The Cancer Genome Atlas dataset detected by univariate Cox regression analysis were further simplified to six by least absolute shrinkage and selection operator (Lasso) and stepwise Akaike information criterion (stepAIC). The study developed a six-gene signature, which was further verified in independent dataset. Association between immune infiltration

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Brunner, Georg, Thomas M. Falk, Beyhan Ertas, Carola Berking, Hans-Joachim Schulze, and Norbert Blödorn-Schlicht. "Validation, in silico and in vitro, of a gene-signature based risk score in cutaneous melanoma." Journal of Clinical Oncology 35, no. 15_suppl (2017): 9560. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.9560.

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9560 Background: Melanoma staging, as defined by the American Joint Committee on Cancer (AJCC), is limited in its ability to predict outcome. We have previously identified and validated a prognostic gene signature expressed in primary cutaneous melanoma and adjacent stroma. The signature comprises seven protective genes (down-regulated with tumor progression) and one risk-associated gene (up-regulated). A signature-based risk score independently predicts patient survival, across AJCC stages IA-IIIC, in formalin-fixed, paraffin-embedded (FFPE) melanomas (training cohort, n = 125; p = 0.0003, ha

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Li, Jian-Rong, Christiana Wang, and Chao Cheng. "Abstract 7663: Identification of high-risk patients in multiple myeloma using a clonal gene signature." Cancer Research 84, no. 6_Supplement (2024): 7663. http://dx.doi.org/10.1158/1538-7445.am2024-7663.

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Abstract Introduction: Multiple myeloma (MM) is a heterogeneous disease with a small subset of high-risk patients associated with poor prognosis. The identification of these patients is crucial for treatment management and strategic decisions. Methods: We developed a novel computational framework that only requires gene expression profiles to define prognostic gene signatures by selecting genes with expression driven by inferred clonal copy number alterations. We applied this framework to MM and developed a clonal gene signature (CGS) consisting of only 22 genes. This prognostic signature was

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Watkins, Paul B. "How to Diagnose and Exclude Drug-Induced Liver Injury." Digestive Diseases 33, no. 4 (2015): 472–76. http://dx.doi.org/10.1159/000374091.

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The diagnosis of drug-induced liver injury (DILI) is largely a diagnosis of exclusion because, with the possible exception of protein:drug adducts in paracetamol overdose, there are no laboratory, biopsy or imaging tests that alone are capable of establishing an unequivocal diagnosis of DILI. However, it is increasingly appreciated that drugs that cause DILI typically have characteristic clinical presentations or ‘signatures' that can be very useful in the diagnosis of DILI. Indeed, knowing a drug's DILI signature (or sometimes signatures) and the incidence rate of DILI during treatment with t

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Chang, Hyun, Seung-Hyun Lee, Taeryool Koo, Moon Ho Kim, and Soo-Yoon Sung. "Prognostic value of hypoxia gene expression in bladder cancer patients." Journal of Clinical Oncology 38, no. 6_suppl (2020): 548. http://dx.doi.org/10.1200/jco.2020.38.6_suppl.548.

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548 Background: The prognostic value of hypoxia in bladder cancer remains unknown. We aimed to evaluate the potential role of hypoxia gene signature as prognostic factors in bladder cancer patients. Methods: We investigated the hypoxia gene signature and clinicopathologic features of The Cancer Genome Atlas (TCGA) bladder urothelial carcinoma (n = 408) using the Kaplan-Meier survival curves and multivariate Cox regression analyses. The clinicopathologic data and the processed data of hypoxia gene signature were obtained from TCGA Bladder urothelial carcinoma database. Results: Hypoxia gene sig

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Raupov, R., E. Suspitsin, R. Mulkidzhan, and M. Kostik. "POS1312 ANALYSIS OF INTERFERON TYPE I SIGNATURE IN JUVENILE DERMATOMYOSITIS." Annals of the Rheumatic Diseases 81, Suppl 1 (2022): 993.3–994. http://dx.doi.org/10.1136/annrheumdis-2022-eular.3292.

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Backgroundthe crucial role of hyperactivation IFN I signaling pathway has been proved in the pathogenesis of dermatomyositis. IFN I genes and chemokines activity vary according to subtype of inflammatory myopathies. IFN type 1 signature could be measured using different genes in the blood, skin and muscle tissue [1].Objectivesto evaluate IFN-score in children with dermatomyositis and compare with disease activityMethods15 patients (5 boys and 10 girls) were enrolled in the study. Clinical and laboratory parameters, disease activity (CMAS-childhood myositis assessment tool, aCAT- abbreviated cu

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Chen, William C., Harish N. Vasudevan, Abrar Choudhury, et al. "A Prognostic Gene-Expression Signature and Risk Score for Meningioma Recurrence After Resection." Neurosurgery 88, no. 1 (2020): 202–10. http://dx.doi.org/10.1093/neuros/nyaa355.

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Abstract BACKGROUND Prognostic markers for meningioma are needed to risk-stratify patients and guide postoperative surveillance and adjuvant therapy. OBJECTIVE To identify a prognostic gene signature for meningioma recurrence and mortality after resection using targeted gene-expression analysis. METHODS Targeted gene-expression analysis was used to interrogate a discovery cohort of 96 meningiomas and an independent validation cohort of 56 meningiomas with comprehensive clinical follow-up data from separate institutions. Bioinformatic analysis was used to identify prognostic genes and generate

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Spiliopoulou, Pavlina, Ming Han, Brooke Wilson, et al. "Global circulating free DNA methylation and fragmentome deconvolution in patients with metastatic renal cell carcinoma treated with immunotherapy (GOLDEN)." Journal of Clinical Oncology 41, no. 16_suppl (2023): 4544. http://dx.doi.org/10.1200/jco.2023.41.16_suppl.4544.

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4544 Background: Metastatic renal cell carcinoma (mRCC) lacks molecular biomarkers. Cell-free methylated DNA immunoprecipitation sequencing (cfMeDIP-seq) is a new, non-invasive approach to the detection of RCC-derived DNA in the circulation. In the GOLDEN study (NCT03702309), we explored the role of cfMeDIP-seq and fragmentomic dynamics as a biomarker, during immune checkpoint inhibition (ICI)-based treatment in mRCC patients (pts). Methods: Blood samples from mRCC pts were collected before and during treatment. Response to treatment was defined by the treating oncologist as: response, any deg

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Dai, Longfei, Xu Wang, Tao Bai, et al. "Identification of a novel cellular senescence-related signature for the prediction of prognosis and immunotherapy response in colon cancer." Frontiers in Genetics 13 (August 4, 2022). http://dx.doi.org/10.3389/fgene.2022.961554.

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The study was conducted to construct a cellular senescence-related risk score signature to predict prognosis and immunotherapy response in colon cancer. Colon cancer data were acquired from the Gene Expression Omnibus and The Cancer Genome Atlas databases. And cellular senescence-related genes were obtained from the CellAge database. The colon cancer data were classified into different clusters based on cellular senescence-related gene expression. Next, prognostic differential genes among clusters were identified with survival analysis. A cellular senescence-related risk score signature was de

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Xing, Aiyan, Dongxiao Lv, Changshun Wu, et al. "Tertiary Lymphoid Structures Gene Signature Predicts Prognosis and Immune Infiltration Analysis in Head and Neck Squamous Cell Carcinoma." Current Genomics 25 (January 29, 2024). http://dx.doi.org/10.2174/0113892029278082240118053857.

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Objectives: This study aims to assess the prognostic implications of gene signature of the tertiary lymphoid structures (TLSs) in head and neck squamous cell carcinoma (HNSCC) and scrutinize the influence of TLS on immune infiltration. Methods: Patients with HNSCC from the Cancer Genome Atlas were categorized into high/low TLS signature groups based on the predetermined TLS signature threshold. The association of the TLS signature with the immune microenvironment, driver gene mutation status, and tumor mutational load was systematically analyzed. Validation was conducted using independent data

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Braune, Eike-Benjamin, Felix Geist, Xiaojia Tang, et al. "Identification of a Notch transcriptomic signature for breast cancer." Breast Cancer Research 26, no. 1 (2024). http://dx.doi.org/10.1186/s13058-023-01757-7.

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Abstract Background Dysregulated Notch signalling contributes to breast cancer development and progression, but validated tools to measure the level of Notch signalling in breast cancer subtypes and in response to systemic therapy are largely lacking. A transcriptomic signature of Notch signalling would be warranted, for example to monitor the effects of future Notch-targeting therapies and to learn whether altered Notch signalling is an off-target effect of current breast cancer therapies. In this report, we have established such a classifier. Methods To generate the signature, we first ident

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Williams, McKenna E., Jeremy A. Elman, Linda K. McEvoy, et al. "12-year prediction of mild cognitive impairment aided by Alzheimer’s brain signatures at mean age 56." Brain Communications, July 23, 2021. http://dx.doi.org/10.1093/braincomms/fcab167.

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Abstract Neuroimaging signatures based on composite scores of cortical thickness and hippocampal volume predict progression from mild cognitive impairment to Alzheimer’s disease. However, little is known about the ability of these signatures among cognitively normal adults to predict progression to mild cognitive impairment. Toward that end, a signature sensitive to microstructural changes that may predate macrostructural atrophy should be useful. We hypothesized that: 1) a validated MRI-derived Alzheimer’s disease signature based on cortical thickness and hippocampal volume in cognitively nor

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