Simultaneous detection of eight cancer types using a multiplex droplet digital PCR assay by Isabelle Neefs, Nele De Meulenaere, Thomas Vanpoucke, Janah Vandenhoeck, Dieter Peeters, Marc Peeters, Guy Van Camp, Ken Op de Beeck

“…Based on previous data analyses using The Cancer Genome Atlas (TCGA), we selected differentially methylated targets for eight frequent tumor types (lung, breast, colorectal, prostate, pancreatic, head and neck, liver, and esophageal cancer). …”
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Serum Cystatin S (CST4): A Novel Prognostic Marker for Gastric Cancer by Chao Gu, Shan Chen, Lining Huang, Chenliang Cao, Renshun Yuan, Zhongyang Kou, Weiwei Chen, Haihua Shi, Xiaodong Gu

“…In addition, CST4 expression was correlated with immune cell infiltration using data from The Cancer Genome Atlas (TCGA). Patients were stratified by median CST4 levels, and Kaplan-Meier curves for OS and DFS were plotted. …”
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Development of a Ten-lncRNA Signature Prognostic Model for Breast Cancer Survival: A Study with the TCGA Database by Wenqing Zhou, Yongkui Pang, Yunmin Yao, Huiying Qiao

“…We downloaded RNA-seq data and relevant clinical information from the Cancer Genome Atlas (TCGA) database. Differentially expressed lncRNA were computed using the “edgeR” package and subjected to the univariate and multivariate Cox regression analysis. …”
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EclipseNB: A Network of Low‐Cost GNSS Receivers to Study the Ionosphere by A. Kashcheyev, B. Nava, C. Watson, P. T. Jayachandran, R. B. Langley

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On Computation of Face Index of Certain Nanotubes by Ansheng Ye, Aisha Javed, Muhammad Kamran Jamil, Kanza Abdul Sattar, Adnan Aslam, Zahid Iqbal, Asfand Fahad

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Investigation of traffic adaptation based interference alignment for cognitive radio networks by Heng DONG, Rong-fang SONG

“…The analysis is verified with statistic model simulation and the results show that the new method outperforms both modified opportunistic IA and fixed IA in average d.o.f and thus can significantly improve the sum capacity of cognitive users without introducing interference to the primary user. …”
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Neutrino Propagation in Matter by Mattias Blennow, Alexei Yu. Smirnov

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Bad suffixes : morphological Pejoration in Old Sicilian by Egle Mocciaro, Roberta Romeo

“…Based on a survey of the ARTESIA corpus, we draw the semantic network of three suffixes inherited, as in other Romance languages, from Latin: -azzu, -a (< Lat. -ācĕus; cf. acquazza 'dirty water'), -astru, -a (< Lat. …”
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Slip Effects on Fractional Viscoelastic Fluids by Muhammad Jamil, Najeeb Alam Khan

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A Metabolic Gene Signature to Predict Overall Survival in Head and Neck Squamous Cell Carcinoma by Zeng-Hong Wu, Yun Tang, Yue Zhou

“…In this study, we used RNA sequencing (RNA-seq) data from the cancer genome atlas (TCGA), with validation in the GEO dataset to profile the metabolic microenvironment and define potential biomarkers for metabolic therapy. …”
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Classification of female MDD patients with and without suicidal ideation using resting-state functional magnetic resonance imaging and machine learning by Morteza Fattahi, Milad Esmaeil-Zadeh, Hamid Soltanian-Zadeh, Hamid Soltanian-Zadeh, Hamid Soltanian-Zadeh, Reza Rostami, Jamil Mansouri, Gholam-Ali Hossein-Zadeh, Gholam-Ali Hossein-Zadeh

“…For our investigation, a brain atlas containing 116 regions of interest was used. …”
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Impact of rare non-coding variants on human diseases through alternative polyadenylation outliers by Xudong Zou, Zhaozhao Zhao, Yu Chen, Kewei Xiong, Zeyang Wang, Shuxin Chen, Hui Chen, Gong-Hong Wei, Shuhua Xu, Wei Li, Ting Ni, Lei Li

“…Here we constructed a comprehensive atlas of alternative polyadenylation (APA) outliers (aOutliers), including 1334 3′ UTR and 200 intronic aOutliers, from 15,201 samples across 49 human tissues. …”
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Integrating bioinformatics and machine learning to identify AhR-related gene signatures for prognosis and tumor microenvironment modulation in melanoma by Qianru Li, Qianru Li, Heli Li

“…However, the specific downstream targets and mechanisms by which AhR influences melanoma remain insufficiently understood.MethodsMelanoma samples from The Cancer Genome Atlas (TCGA) and normal skin tissues from the Genotype-Tissue Expression (GTEx) database were analyzed to identify differentially expressed genes, which were intersected with a curated list of AhR-related pathway genes. …”
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Pre-service teachers' perceptions of physical, socioemotional and cognitive traits in gifted students: unveiling bias? by Carmen Ferrándiz, Mercedes Ferrando-Prieto, Álvaro Infantes-Paniagua, Álvaro Infantes-Paniagua, Maria Carmen Fernández Vidal, Rosa María Pons

“…The study delves specifically into the physical characteristics attributed to gifted students through qualitative analysis addressing co-occurrence coefficients (Atlas.ti 9).ResultsThe results suggest that pre-service teachers described gifted students as more intelligent and with better physical attributes, especially gifted females. …”
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Identification of MYC and STAT3 for early diagnosis based on the long noncoding RNA-mRNA network and bioinformatics in colorectal cancer by Kunhou Yao, Hao Fan, Tiancheng Yang, Can Yang, Guibin Wang, Xingwang Li, Xin-Ying Ji, Qun Wang, Shaojiang Lv, Shihao Guo

“…Quantitative Real-Time PCR (qRT-PCR) was performed to validate MYC and STAT3 expression levels, and findings were further confirmed using the Human Protein Atlas (HPA) database.ResultsDatabase analysis revealed significant differential expression of MYC and STAT3 between normal and CRC tissues. …”
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Integrating RNA-seq and scRNA-seq to explore the prognostic features and immune landscape of exosome-related genes in breast cancer metastasis by Guanyou Huang, Yong Yu, Heng Su, Hongchuan Gan, Liangzhao Chu

“…Objective This study aims to explore the role of exosome-related genes in breast cancer (BRCA) metastasis by integrating RNA-seq and single-cell RNA-seq (scRNA-seq) data from BRCA samples and to develop a reliable prognostic model.Methods Initially, a comprehensive analysis was conducted on exosome-related genes from the BRCA cohort in The Cancer Genome Atlas (TCGA) database. Three prognostic genes (JUP, CAPZA1 and ARVCF) were identified through univariate Cox regression and Lasso-Cox regression analyses, and a metastasis-related risk score model was established based on these genes. …”
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Patient Perspectives on Personalized Risk Communication Using Polygenic Risk Scores to Inform Colorectal Cancer Screening Decisions by Shauna R. Goldberg, MPH, Linda K. Ko, PhD, Li Hsu, PhD, Hang Yin, MS, Charles Kooperberg, PhD, Ulrike Peters, PhD, MPH, Andrea N. Burnett-Hartman, PhD, MPH

“…Thematic analysis was conducted using Atlas.ti, Version 8. Results: Of the 19 participants who successfully completed polygenic risk score–related testing and a semistructured interview, 13 were female, 14 never smoked cigarettes, 6 were Hispanic, and 13 were non-Hispanic White. …”
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Aberrant DNA Methylation of FTH-1 and SHOX2 Contributes to Lung Cancer Progression by Salar Tahmasbi, Armin Sadeghi, Habib Zarredar, Milad Asadi, Shahryar Hashemzadeh, Venus Zafari, Hamed Sabbagh-Jadid, Mortaza Raeisi

“…Ferritin heavy chain 1 (FTH-1) and SHOX homeobox 2 (SHOX2) DNA methylation were investigated in non-small cell lung cancer (NSCLC) as novel epigenetic biomarkers.Method: In this case-control study, we initially evaluated the diagnostic value of FTH-1 and SHOX2 DNA methylation, and the Cancer Genome Atlas (TCGA) data on the methylation profile of NSCLC was analyzed. …”
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The heterogeneity of NOTCH1 to tumor immune infiltration in pan-cancer by XiaoJun Duan, Rihan Wu, Mingyang Zhang, Kexin Li, Lei Yu, Huirong Sun, Xingxia Hao, Changshan Wang

“…In this study, the data originated from the Genotype-Tissue Expression (GTEx) and the Cancer Genome Atlas (TCGA) databases were input into multiple online bioinformatic tools to study the characteristics of NOTCH1 in pan-cancer. …”
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NIPAL1 as a prognostic biomarker associated with pancreatic adenocarcinoma progression and immune infiltration by Youlong Zhu, Zongze Qi, Shaoqi Zu, Fangchao Yang, Yanming Wang, Lei Zhu, Xintong Li, Ruixue Li, Hong Zhu

“…Primitive RNA sequencing (RNA-seq) data of PAAD from The Cancer Genome Atlas (TCGA) was utilized for bioinformatics analysis to characterize the expression levels of NIPAL1 in tumor and normal tissues. …”
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