Discovery and validation of methylation signatures in blood-based circulating tumor cell-free DNA in early detection of colorectal carcinoma: a case–control study

Abstract Background Early detection of colorectal carcinoma (CRC) would help to identify tumors when curative treatments are available and beneficial. However, current screening methods for CRC, e.g., colonoscopy, may affect patients’ compliance due to the uncomfortable, invasive and time-consuming process. In recent decades, methylation profiles of blood-based circulating tumor DNA (ctDNA) have shown promising results in the early detection of multiple tumors. Here we conducted a study to investigate the performance of ctDNA methylation markers in early detection of CRC. Results In total, 742 participants were enrolled in the study including CRC (n = 332), healthy control (n = 333), benign colorectal disease (n = 65) and advanced adenoma (n = 12). After age-matched and randomization, 298 participants (149 cancer and 149 healthy control) were included in training set and 141 (67 cancer and 74 healthy control) were in test set. In the training set, the specificity was 89.3% (83.2–93.7%) and the sensitivity was 88.6% (82.4–93.2%). In terms of different stages, the sensitivities were 79.4% (62.1–91.2%) in patients with stage I, 88.9% (77.3–95.8%) in patients with stage II, 91.4% (76.9–98.2%) in patients with stage III and 96.2% (80.3–99.9%) in patients with stage IV. Similar results were validated in the test set with the specificity of 91.9% (83.1–97.0%) and sensitivity of 83.6% (72.5–91.6%). Sensitivities for stage I-III were 87.0% (79.7–92.4%) in the training set and 82.5% (70.2–91.3%) in the test set, respectively. In the unmatched total population, the positive ratios were 7.8% (5.2–11.2%) in healthy control, 30.8% (19.9–43.5%) in benign colorectal disease and 58.3% (27.5–84.7%) in advanced adenoma, while the sensitivities of stage I–IV were similar with training and test sets. Compared with methylated SEPT9 model, the present model had higher sensitivity (87.0% [81.8–91.2%] versus 41.2% [34.6–48.1%], P < 0.001) under comparable specificity (90.1% [85.4–93.7%] versus 90.6% [86.0–94.1%]). Conclusions Together our findings showed that ctDNA methylation markers were promising in the early detection of CRC. Further validation of this model is warranted in prospective studies.

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Discovering Panel of Autoantibodies for Early Detection of Lung Cancer Based on Focused Protein Array

Frontiers in Immunology ◽

10.3389/fimmu.2021.658922 ◽

2021 ◽

Vol 12 ◽

Author(s):

Di Jiang ◽

Xue Zhang ◽

Man Liu ◽

Yulin Wang ◽

Tingting Wang ◽

...

Keyword(s):

Lung Cancer ◽

Early Detection ◽

Validation Cohort ◽

Protein Array ◽

Enzyme Linked Immunosorbent Assay ◽

Training Set ◽

Driver Genes ◽

Test Set ◽

Cancer Driver ◽

Cancer Driver Genes

Substantial studies indicate that autoantibodies to tumor-associated antigens (TAAbs) arise in early stage of lung cancer (LC). However, since single TAAbs as non-invasive biomarkers reveal low diagnostic performances, a panel approach is needed to provide more clues for early detection of LC. In the present research, potential TAAbs were screened in 150 serum samples by focused protein array based on 154 proteins encoded by cancer driver genes. Indirect enzyme-linked immunosorbent assay (ELISA) was used to verify and validate TAAbs in two independent datasets with 1,054 participants (310 in verification cohort, 744 in validation cohort). In both verification and validation cohorts, eight TAAbs were higher in serum of LC patients compared with normal controls. Moreover, diagnostic models were built and evaluated in the training set and the test set of validation cohort by six data mining methods. In contrast to the other five models, the decision tree (DT) model containing seven TAAbs (TP53, NPM1, FGFR2, PIK3CA, GNA11, HIST1H3B, and TSC1), built in the training set, yielded the highest diagnostic value with the area under the receiver operating characteristic curve (AUC) of 0.897, the sensitivity of 94.4% and the specificity of 84.9%. The model was further assessed in the test set and exhibited an AUC of 0.838 with the sensitivity of 89.4% and the specificity of 78.2%. Interestingly, the accuracies of this model in both early and advanced stage were close to 90%, much more effective than that of single TAAbs. Protein array based on cancer driver genes is effective in screening and discovering potential TAAbs of LC. The TAAbs panel with TP53, NPM1, FGFR2, PIK3CA, GNA11, HIST1H3B, and TSC1 is excellent in early detection of LC, and they might be new target in LC immunotherapy.

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PREDIKSI KUALITAS AIR SUNGAI CILIWUNG DENGAN MENGGUNAKAN ALGORITMA POHON KEPUTUSAN

Jurnal Air Indonesia ◽

10.29122/jai.v12i2.4364 ◽

2021 ◽

Vol 12 (2) ◽

Author(s):

Mohammad Haekal ◽

Henki Bayu Seta ◽

Mayanda Mega Santoni

Keyword(s):

Data Mining ◽

Decision Tree ◽

Cross Validation ◽

Online Monitoring ◽

Training Set ◽

Microsoft Excel ◽

Test Set

Untuk memprediksi kualitas air sungai Ciliwung, telah dilakukan pengolahan data-data hasil pemantauan secara Online Monitoring dengan menggunakan Metode Data Mining. Pada metode ini, pertama-tama data-data hasil pemantauan dibuat dalam bentuk tabel Microsoft Excel, kemudian diolah menjadi bentuk Pohon Keputusan yang disebut Algoritma Pohon Keputusan (Decision Tree) mengunakan aplikasi WEKA. Metode Pohon Keputusan dipilih karena lebih sederhana, mudah dipahami dan mempunyai tingkat akurasi yang sangat tinggi. Jumlah data hasil pemantauan kualitas air sungai Ciliwung yang diolah sebanyak 5.476 data. Hasil klarifikasi dengan Pohon Keputusan, dari 5.476 data ini diperoleh jumlah data yang mengindikasikan sungai Ciliwung Tidak Tercemar sebanyak 1.059 data atau sebesar 19,3242%, dan yang mengindikasikan Tercemar sebanyak 4.417 data atau 80,6758%. Selanjutnya data-data hasil pemantauan ini dievaluasi menggunakan 4 Opsi Tes (Test Option) yaitu dengan Use Training Set, Supplied Test Set, Cross-Validation folds 10, dan Percentage Split 66%. Hasil evaluasi dengan 4 opsi tes yang digunakan ini, semuanya menunjukkan tingkat akurasi yang sangat tinggi, yaitu diatas 99%. Dari data-data hasil peneltian ini dapat diprediksi bahwa sungai Ciliwung terindikasi sebagai sungai tercemar bila mereferensi kepada Peraturan Pemerintah Republik Indonesia nomor 82 tahun 2001 dan diketahui pula bahwa penggunaan aplikasi WEKA dengan Algoritma Pohon Keputusan untuk mengolah data-data hasil pemantauan dengan mengambil tiga parameter (pH, DO dan Nitrat) adalah sangat akuran dan tepat. Kata Kunci : Kualitas air sungai, Data Mining, Algoritma Pohon Keputusan, Aplikasi WEKA.

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Faculty Opinions recommendation of Immunohistochemical assessment of lymphovascular invasion in stage I colorectal carcinoma: prognostic relevance and correlation with nodal micrometastases.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.13489149.14866304 ◽

2012 ◽

Author(s):

Noam Harpaz ◽

Alexandros Polydorides

Keyword(s):

Colorectal Carcinoma ◽

Lymphovascular Invasion ◽

Stage I ◽

Prognostic Relevance ◽

Immunohistochemical Assessment

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QSPR modelling of the octanol/water partition coefficient of organometallic substances by optimal SMILES-based descriptors

Open Chemistry ◽

10.2478/s11532-009-0095-y ◽

2009 ◽

Vol 7 (4) ◽

pp. 846-856 ◽

Cited By ~ 6

Author(s):

Andrey Toropov ◽

Alla Toropova ◽

Emilio Benfenati

Keyword(s):

Partition Coefficient ◽

Organometallic Compounds ◽

Applicability Domain ◽

Training Set ◽

Input Line ◽

Test Set ◽

Water Partition Coefficient ◽

Definition Of

AbstractUsually, QSPR is not used to model organometallic compounds. We have modeled the octanol/water partition coefficient for organometallic compounds of Na, K, Ca, Cu, Fe, Zn, Ni, As, and Hg by optimal descriptors calculated with simplified molecular input line entry system (SMILES) notations. The best model is characterized by the following statistics: n=54, r2=0.9807, s=0.677, F=2636 (training set); n=26, r2=0.9693, s=0.969, F=759 (test set). Empirical criteria for the definition of the applicability domain for these models are discussed.

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Feature-Weighted Sampling for Proper Evaluation of Classification Models

Applied Sciences ◽

10.3390/app11052039 ◽

2021 ◽

Vol 11 (5) ◽

pp. 2039

Author(s):

Hyunseok Shin ◽

Sejong Oh

Keyword(s):

Random Sampling ◽

Sampling Method ◽

Classification Model ◽

Training Set ◽

Test Set ◽

Feature Importance ◽

Proper Training ◽

Machine Learning Applications ◽

Test Sets ◽

The Given

In machine learning applications, classification schemes have been widely used for prediction tasks. Typically, to develop a prediction model, the given dataset is divided into training and test sets; the training set is used to build the model and the test set is used to evaluate the model. Furthermore, random sampling is traditionally used to divide datasets. The problem, however, is that the performance of the model is evaluated differently depending on how we divide the training and test sets. Therefore, in this study, we proposed an improved sampling method for the accurate evaluation of a classification model. We first generated numerous candidate cases of train/test sets using the R-value-based sampling method. We evaluated the similarity of distributions of the candidate cases with the whole dataset, and the case with the smallest distribution–difference was selected as the final train/test set. Histograms and feature importance were used to evaluate the similarity of distributions. The proposed method produces more proper training and test sets than previous sampling methods, including random and non-random sampling.

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A highly expressed mRNA signature for predicting survival in patients with stage I/II non-small-cell lung cancer after operation

Scientific Reports ◽

10.1038/s41598-021-85246-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Nan Ma ◽

Lu Si ◽

Meiling Yang ◽

Meihua Li ◽

Zhiyi He

Keyword(s):

High Risk ◽

Roc Curve ◽

Expression Profiles ◽

Low Risk ◽

Stage I ◽

Risk Scores ◽

Training Set ◽

Cox Regression Analysis ◽

Nsclc Patients ◽

Rna Signature

AbstractThere is an urgent need to identify novel biomarkers that predict the prognosis of patients with NSCLC. In this study,we aim to find out mRNA signature closely related to the prognosis of NSCLC by new algorithm of bioinformatics. Identification of highly expressed mRNA in stage I/II patients with NSCLC was performed with the “Limma” package of R software. Survival analysis of patients with different mRNA expression levels was subsequently calculated by Cox regression analysis, and a multi-RNA signature was obtained by using the training set. Kaplan–Meier estimator, log-rank test and receiver operating characteristic (ROC) curves were used to analyse the predictive ability of the multi-RNA signature. RT-PCR used to verify the expression of the multi-RNA signature, and Westernblot used to verify the expression of proteins related to the multi-RNA signature. We identified fifteen survival-related mRNAs in the training set and classified the patients as high risk or low risk. NSCLC patients with low risk scores had longer disease-free survival than patients with high risk scores. The fifteen-mRNA signature was an independent prognostic factor, as shown by the ROC curve. ROC curve also showed that the combined model of the fifteen-mRNA signature and tumour stage had higher precision than stage alone. The expression of fifteen mRNAs and related proteins were higher in stage II NSCLC than in stage I NSCLC. Multi-gene expression profiles provide a moderate prognostic tool for NSCLC patients with stage I/II disease.

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Serum N-glycan profiles differ for various breast cancer subtypes

Glycoconjugate Journal ◽

10.1007/s10719-021-10001-3 ◽

2021 ◽

Author(s):

Gerda C. M. Vreeker ◽

Kiki M. H. Vangangelt ◽

Marco R. Bladergroen ◽

Simone Nicolardi ◽

Wilma E. Mesker ◽

...

Keyword(s):

Breast Cancer ◽

Early Detection ◽

Cancer Patients ◽

High Resolution Mass Spectrometry ◽

Case Control ◽

Total Serum ◽

Screening Methods ◽

Breast Cancer Patients ◽

Cancer Subtypes ◽

Control Comparison

AbstractBreast cancer is the most prevalent cancer in women. Early detection of this disease improves survival and therefore population screenings, based on mammography, are performed. However, the sensitivity of this screening modality is not optimal and new screening methods, such as blood tests, are being explored. Most of the analyses that aim for early detection focus on proteins in the bloodstream. In this study, the biomarker potential of total serum N-glycosylation analysis was explored with regard to detection of breast cancer. In an age-matched case-control setup serum protein N-glycan profiles from 145 breast cancer patients were compared to those from 171 healthy individuals. N-glycans were enzymatically released, chemically derivatized to preserve linkage-specificity of sialic acids and characterized by high resolution mass spectrometry. Logistic regression analysis was used to evaluate associations of specific N-glycan structures as well as N-glycosylation traits with breast cancer. In a case-control comparison three associations were found, namely a lower level of a two triantennary glycans and a higher level of one tetraantennary glycan in cancer patients. Of note, various other N-glycomic signatures that had previously been reported were not replicated in the current cohort. It was further evaluated whether the lack of replication of breast cancer N-glycomic signatures could be partly explained by the heterogenous character of the disease since the studies performed so far were based on cohorts that included diverging subtypes in different numbers. It was found that serum N-glycan profiles differed for the various cancer subtypes that were analyzed in this study.

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Weakly supervised deep learning for determining the prognostic value of 18F-FDG PET/CT in extranodal natural killer/T cell lymphoma, nasal type

European Journal of Nuclear Medicine and Molecular Imaging ◽

10.1007/s00259-021-05232-3 ◽

2021 ◽

Author(s):

Rui Guo ◽

Xiaobin Hu ◽

Haoming Song ◽

Pengpeng Xu ◽

Haoping Xu ◽

...

Keyword(s):

Deep Learning ◽

Fdg Pet ◽

Cell Lymphoma ◽

Training Set ◽

Test Set ◽

Natural Killer T Cell ◽

Pet Ct ◽

Weakly Supervised ◽

Fdg Pet Ct ◽

Killer T Cell

Abstract Purpose To develop a weakly supervised deep learning (WSDL) method that could utilize incomplete/missing survival data to predict the prognosis of extranodal natural killer/T cell lymphoma, nasal type (ENKTL) based on pretreatment 18F-FDG PET/CT results. Methods One hundred and sixty-seven patients with ENKTL who underwent pretreatment 18F-FDG PET/CT were retrospectively collected. Eighty-four patients were followed up for at least 2 years (training set = 64, test set = 20). A WSDL method was developed to enable the integration of the remaining 83 patients with incomplete/missing follow-up information in the training set. To test generalization, these data were derived from three types of scanners. Prediction similarity index (PSI) was derived from deep learning features of images. Its discriminative ability was calculated and compared with that of a conventional deep learning (CDL) method. Univariate and multivariate analyses helped explore the significance of PSI and clinical features. Results PSI achieved area under the curve scores of 0.9858 and 0.9946 (training set) and 0.8750 and 0.7344 (test set) in the prediction of progression-free survival (PFS) with the WSDL and CDL methods, respectively. PSI threshold of 1.0 could significantly differentiate the prognosis. In the test set, WSDL and CDL achieved prediction sensitivity, specificity, and accuracy of 87.50% and 62.50%, 83.33% and 83.33%, and 85.00% and 75.00%, respectively. Multivariate analysis confirmed PSI to be an independent significant predictor of PFS in both the methods. Conclusion The WSDL-based framework was more effective for extracting 18F-FDG PET/CT features and predicting the prognosis of ENKTL than the CDL method.

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Utilizing two-tiered screening for early detection of autism spectrum disorder

Autism ◽

10.1177/1362361317712649 ◽

2017 ◽

Vol 22 (7) ◽

pp. 881-890 ◽

Cited By ~ 11

Author(s):

Meena Khowaja ◽

Diana L Robins ◽

Lauren B Adamson

Keyword(s):

Early Detection ◽

Young Children ◽

False Positive ◽

Screening Tool ◽

False Positive Rate ◽

Autism Spectrum ◽

Screening Methods ◽

Positive Rate ◽

Level 1 ◽

Level 2

Despite advances in autism screening practices, challenges persist, including barriers to implementing universal screening in primary care and difficulty accessing services. The high false positive rate of Level 1 screening methods presents especially daunting difficulties because it increases the need for comprehensive autism evaluations. This study explored whether two-tiered screening—combining Level 1 (Modified Checklist for Autism in Toddlers, Revised with Follow-Up) and Level 2 (Screening Tool for Autism in Toddlers and Young Children) measures—improves the early detection of autism. This study examined a sample of 109 toddlers who screened positive on Level 1 screening and completed a Level 2 screening measure prior to a diagnostic evaluation. Results indicated that two-tiered screening reduced the false positive rate using published Screening Tool for Autism in Toddlers and Young Children cutoffs compared to Level 1 screening alone, although at a cost to sensitivity. However, alternative Screening Tool for Autism in Toddlers and Young Children scoring in the two-tiered screening improved both positive predictive value and sensitivity. Exploratory analyses were conducted, including comparison of autism symptoms and clinical profiles across screening subsamples. Recommendations regarding clinical implications of two-tiered screening and future areas of research are presented.

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