scholarly journals KLF3 and PAX6 are candidate driver genes in late-stage, MSI-hypermutated endometrioid endometrial carcinomas

2021 ◽  
Author(s):  
Meghan L. Rudd ◽  
Nancy F. Hansen ◽  
Xiaolu Zhang ◽  
Mary Ellen Urick ◽  
Suiyuan Zhang ◽  
...  
Keyword(s):  
Late Stage ◽  
Statistical Power ◽  
Target Genes ◽  
Early Stage ◽  
Uterine Cancer ◽  
The Cancer Genome Atlas ◽  
Driver Genes ◽  
P Values ◽  
Endometrial Carcinomas ◽  
Stage Stage

AbstractEndometrioid endometrial carcinomas (EECs) are the most common histological subtype of uterine cancer. Late-stage disease is an adverse prognosticator for EEC. The purpose of this study was to analyze EEC exome mutation data to identify late-stage-specific statistically significantly mutated genes (SMGs), which represent candidate driver genes potentially associated with disease progression. We exome sequenced 15 late-stage (stage III or IV) non-ultramutated EECs and paired non-tumor DNAs; somatic variants were called using Strelka, Shimmer, Somatic Sniper and MuTect. Additionally, somatic mutation calls were extracted from The Cancer Genome Atlas (TCGA) data for 66 late-stage and 270 early-stage (stage I or II) non-ultramutated EECs. MutSigCV (v1.4) was used to annotate SMGs in the two late-stage cohorts and to derive p-values for all mutated genes in the early-stage cohort. To test whether late-stage SMGs are statistically significantly mutated in early-stage tumors, q-values for late-stage SMGs were re-calculated from the MutSigCV (v1.4) early-stage p-values, adjusting for the number of late-stage SMGs tested. We identified 14 SMGs in the combined late-stage EEC cohorts. When the 14 late-stage SMGs were examined in the TCGA early-stage data, only KLF3 and PAX6 failed to reach significance as early-stage SMGs, despite the inclusion of enough early-stage cases to ensure adequate statistical power. Within TCGA, nonsynonymous mutations in KLF3 and PAX6 were, respectively, exclusive or nearly exclusive to the microsatellite instability (MSI)-hypermutated molecular subgroup and were dominated by insertions-deletions at homopolymer tracts. In conclusion, our findings are hypothesis-generating and suggest that KLF3 and PAX6, which encode transcription factors, are MSI target genes and late-stage-specific SMGs in EEC.

scholarly journals Expression based biomarkers and models to classify early and late stage samples of Papillary Thyroid Carcinoma

2018 ◽  
Author(s):  
Sherry Bhalla ◽  
Harpreet Kaur ◽  
Rishemjit Kaur ◽  
Suresh Sharma ◽  
Gajendra P. S. Raghava
Keyword(s):  
Late Stage ◽  
Early Stage ◽  
Characteristic Curve ◽  
The Cancer Genome Atlas ◽  
Validation Dataset ◽  
Receptor Interaction ◽  
Papillary Thyroid ◽  
Protein Coding ◽  
Independent Validation ◽  
Multiple Transcripts

AbstractIn this study, we describe the key transcripts and machine learning models developed for classifying the early and late stage samples of Papillary Thyroid Cancer (PTC), using transcripts’ expression data from The Cancer Genome Atlas (TCGA). First, we rank all the transcripts on the basis of area under receiver operating characteristic curve, (AUROC) value to discriminate the early and late stage, based on an expression threshold. With the expression of a single transcript DCN, we can classify the stage samples with a 68.5% accuracy and AUROC of 0.66. Then we implemented various combination of multiple gene panels, selected using various gold standard feature selection techniques. The model based on the expression of 36 multiple transcripts (protein coding and non-coding) selected using SVC-L1 achieves the maximum accuracy of 74.51% with AUROC of 0.75 on independent validation dataset with balanced sensitivity and specificity. Further, these signatures also performed well on external microarray data obtained from GEO, predicting nearly 70% (12 samples out of 17 samples) early stage samples correctly. Further, multiclass model, classifying the normal, early and late stage samples achieves the accuracy of 75.43% with AUROC of 0.80 on independent validation dataset. With correlation analysis, we found that transcripts with maximum change in correlation of their expression in both the stages are significantly enriched in neuroactive ligand receptor interaction pathway. We also propose a panel of five protein coding transcripts, which on the basis of their expression, can segregate cancer and normal samples with 97.32% accuracy and AUROC of 0.99 on independent validation dataset. All the models and dataset used in this study are available from the web server CancerTSP (http://webs.iiitd.edu.in/raghava/cancertsp/).

scholarly journals Characterization of a five-microRNA signature as a prognostic biomarker for esophageal squamous cell carcinoma

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Jun Yu ◽  
Ming Zhu ◽  
Min Lv ◽  
Xiaoliu Wu ◽  
Xiaomei Zhang ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Late Stage ◽  
Target Genes ◽  
Cox Regression ◽  
Risk Group ◽  
Early Stage ◽  
Training Set

AbstractThis study aims to identify a miRNAs signature for predicting overall survival (OS) in esophageal squamous cell carcinoma (ESCC) patients. MiRNA expression profiles and corresponding clinical information of 119 ESCC patients were obtained from NCBI GEO and used as the training set. Differentially expressed miRNAs (DEmiRNAs) were screened between early-stage and late-stage samples. Cox regression analysis, recursive feature elimination (RFE)-support vector machine (SVM) algorithm, and LASSO Cox regression model were used to identify prognostic miRNAs and consequently build a prognostic scoring model. Moreover, promising target genes of these prognostic miRNAs were predicted followed by construction of miRNA-target gene networks. Functional relevance of predicted target genes of these prognostic miRNAs in ESCC was analyzed by performing function enrichment analyses. There were 46 DEmiRNAs between early-stage and late-stage samples in the training set. A risk score model based on five miRNAs was built. The five-miRNA risk score could classify the training set into a high-risk group and a low-risk group with significantly different OS time. Risk stratification ability of the five-miRNA risk score was successfully validated on an independent set from the Cancer Genome Atlas (TCGA). Various biological processes and pathways were identified to be related to these miRNAs, such as Wnt signaling pathway, inflammatory mediator regulation of TRP channels pathway, and estrogen signaling pathway. The present study suggests a pathological stage-related five-miRNA signature that may have clinical implications in predicting prognosis of ESCC patients.

scholarly journals Genomic profiling of microRNA target genes in colorectal cancer

Tumor Biology ◽  
2020 ◽  
Vol 42 (6) ◽  
pp. 101042832093351
Author(s):  
Adewale Oluwaseun Fadaka ◽  
Olalekan Olanrewaju Bakare ◽  
Ashley Pretorius ◽  
Ashwil Klein
Keyword(s):  
Colorectal Cancer ◽  
Protein Interaction ◽  
Tyrosine Kinases ◽  
Target Genes ◽  
Genetic Mutation ◽  
Tumor Formation ◽  
The Cancer Genome Atlas ◽  
Microrna Target ◽  
Driver Genes ◽  
Protein Protein Interaction

Colorectal cancer is the second and third most common cancer in men and women, respectively, worldwide. Alterations such as genetic and epigenetic are common in colorectal cancer and are the basis of tumor formation. The exploration of the molecular basis of colorectal cancer can drive a better understanding of the disease as well as guide the prognosis, therapeutics, and disease management. This study is aimed at investigating the genetic mutation profile of five candidate microRNAs (hsa-miR-513b-3p, hsa-miR-500b-3p, hsa-miR-500a-3p, hsa-miR-450b-3p, hsa-miR-193a-5p) targeted by seven genes (APC, KRAS, TCF7L2, EGFR, IGF1R, CASP8, and GNAS)) using in silico approaches. Two datasets (dataset 1 from our previous study and dataset two (The Cancer Genome Atlas, Nature 2012) were considered for this study. Protein–protein interaction, expression analysis, and genetic profiling were carried out using STRING, FireBrowse, and cBioPortal, respectively. Protein–protein interaction network showed that epidermal growth factor receptor has the highest connection among the target genes and this can be considered as the hub gene. Relative to other solid tumors, in colorectal cancer, six of the target genes were downregulated and only CASP8 was upregulated. Genes with protein tyrosine kinases domain were frequently altered in colorectal cancer and the most common alteration in these genes/domain are missense mutation. These results could serve as a lead in the identification of driver genes responsible for colorectal cancer initiation and progression. However, the intense mechanism of these results remains unclear and further experimental validation and molecular approaches are the focal points in the nearest future.

scholarly journals Gene-Microbiome Co-expression Networks in Colon Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Irving Uriarte-Navarrete ◽  
Enrique Hernández-Lemus ◽  
Guillermo de Anda-Jáuregui
Keyword(s):  
Gene Expression ◽  
Colon Cancer ◽  
Late Stage ◽  
Early Stage ◽  
Functional Divergence ◽  
The Cancer Genome Atlas ◽  
Bipartite Network ◽  
Atlas Collaboration ◽  
Microbial Abundance ◽  
Bipartite Networks

It is known that cancer onset and development arise from complex, multi-factorial phenomena spanning from the molecular, functional, micro-environmental, and cellular up to the tissular and organismal levels. Important advances have been made in the systematic analysis of the molecular (mostly genomic and transcriptomic) within large studies of high throughput data such as The Cancer Genome Atlas collaboration. However, the role of the microbiome in the induction of biological changes needed to reach these pathological states remains to be explored, largely because of scarce experimental data. In recent work a non-standard bioinformatics strategy was used to indirectly quantify microbial abundance from TCGA RNA-seq data, allowing the evaluation of the microbiome in well-characterized cancer patients, thus opening the way to studies incorporating the molecular and microbiome dimensions altogether. In this work, we used such recently described approaches for the quantification of microbial species alongside with gene expression. With this, we will reconstruct bipartite networks linking microbial abundance and gene expression in the context of colon cancer, by resorting to network reconstruction based on measures from information theory. The rationale is that microbial communities may induce biological changes important for the cancerous state. We analyzed changes in microbiome-gene interactions in the context of early (stages I and II) and late (stages III and IV) colon cancer, studied changes in network descriptors, and identify key discriminating features for early and late stage colon cancer. We found that early stage bipartite network is associated with the establishment of structural features in the tumor cells, whereas late stage is related to more advance signaling and metabolic features. This functional divergence thus arise as a consequence of changes in the organization of the corresponding gene-microorganism co-expression networks.

scholarly journals MEGSA: A powerful and flexible framework for analyzing mutual exclusivity of tumor mutations

2015 ◽  
Author(s):  
Xing Hua ◽  
Paula L. Hyland ◽  
Jing Huang ◽  
Bin Zhu ◽  
Neil E. Caporaso ◽  
...  
Keyword(s):  
Statistical Power ◽  
De Novo ◽  
The Cancer Genome Atlas ◽  
Driver Mutations ◽  
Ratio Test ◽  
Mutual Exclusivity ◽  
Driver Genes ◽  
Functional Relationships ◽  
Cancer Types ◽  
Tumor Sequencing

The central challenge in tumor sequencing studies is to identify driver genes and pathways, investigate their functional relationships and nominate drug targets. The efficiency of these analyses, particularly for infrequently mutated genes, is compromised when patients carry different combinations of driver mutations. Mutual exclusivity analysis helps address these challenges. To identify mutually exclusive gene sets (MEGS), we developed a powerful and flexible analytic framework based on a likelihood ratio test and a model selection procedure. Extensive simulations demonstrated that our method outperformed existing methods for both statistical power and the capability of identifying the exact MEGS, particularly for highly imbalanced MEGS. Our method can be used for de novo discovery, pathway-guided searches or for expanding established small MEGS. We applied our method to the whole exome sequencing data for fourteen cancer types from The Cancer Genome Atlas (TCGA). We identified multiple previously unreported non-pairwise MEGS in multiple cancer types. For acute myeloid leukemia, we identified a novel MEGS with five genes (FLT3, IDH2, NRAS, KIT and TP53) and a MEGS (NPM1, TP53 and RUX1) whose mutation status was strongly associated with survival (P=6.7×10-4). For breast cancer, we identified a significant MEGS consisting of TP53 and four infrequently mutated genes (ARID1A, AKT1, MED23 and TBL1XR1), providing support for their role as cancer drivers. Keywords: Mutual exclusivity, oncogenic pathways, driver genes, tumor sequencing

scholarly journals An Eight-CpG-based Methylation Classifier for Preoperative Discriminating Early and Advanced-Late Stage of Colorectal Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Ji Hu ◽  
Fu-ying Zhao ◽  
Bin Huang ◽  
Jing Ran ◽  
Mei-yuan Chen ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Late Stage ◽  
Risk Group ◽  
Early Stage ◽  
Methylation Status ◽  
Preoperative Staging ◽  
The Cancer Genome Atlas ◽  
Lasso Regression ◽  
Multivariable Logistic Regression Model ◽  
Prognostic Information

AimTo develop and validate a CpG-based classifier for preoperative discrimination of early and advanced-late stage colorectal cancer (CRC).MethodsWe identified an epigenetic signature based on methylation status of multiple CpG sites (CpGs) from 372 subjects in The Cancer Genome Atlas (TCGA) CRC cohort, and an external cohort (GSE48684) with 64 subjects by LASSO regression algorithm. A classifier derived from the methylation signature was used to establish a multivariable logistic regression model to predict the advanced-late stage of CRC. A nomogram was further developed by incorporating the classifier and some independent clinical risk factors, and its performance was evaluated by discrimination and calibration analysis. The prognostic value of the classifier was determined by survival analysis. Furthermore, the diagnostic performance of several CpGs in the methylation signature was evaluated.ResultsThe eight-CpG-based methylation signature discriminated early stage from advanced-late stage CRC, with a satisfactory AUC of more than 0.700 in both the training and validation sets. This methylation classifier was identified as an independent predictor for CRC staging. The nomogram showed favorable predictive power for preoperative staging, and the C-index reached 0.817 (95% CI: 0.753–0.881) and 0.817 (95% CI: 0.721–0.913) in another training set and validation set respectively, with good calibration. The patients stratified in the high-risk group by the methylation classifier had significantly worse survival outcome than those in the low-risk group. Combination diagnosis utilizing only four of the eight specific CpGs performed well, even in CRC patients with low CEA level or at early stage.ConclusionsOur classifier is a valuable predictive indicator that can supplement established methods for more accurate preoperative staging and also provides prognostic information for CRC patients. Besides, the combination of multiple CpGs has a high value in the diagnosis of CRC.

scholarly journals Computing MicroRNA-Gene Interaction Networks in Pan-cancer Using MiRDriver

2021 ◽  
Author(s):  
Banabithi Bose ◽  
Matthew Moravec ◽  
Serdar Bozdag
Keyword(s):  
Copy Number ◽  
Target Genes ◽  
The Cancer Genome Atlas ◽  
Gene Interactions ◽  
Microrna Target ◽  
Multiple Cancer ◽  
Driver Genes ◽  
Microrna Gene ◽  
Cancer Types ◽  
Pan Cancer

Abstract DNA copy number aberrated regions in cancer are known to harbor cancer driver genes and the short non-coding RNA molecules, i.e., microRNAs. In this study, we integrated the multi-omics datasets such as copy number aberration, DNA methylation, gene and microRNA expression to identify the signature microRNA-gene associations from frequently aberrated DNA regions across pan-cancer utilizing a LASSO-based regression approach. We studied 7,294 patient samples associated with eighteen different cancer types from The Cancer Genome Atlas (TCGA) database and identified several cancer-specific microRNA-gene interactions enriched in experimentally validated microRNA-target databases. We highlighted several oncogenic and tumor suppressor microRNAs and genes that were common in several cancer types. Our method substantially outperformed the five state-of-art methods in selecting significantly known microRNA-gene interactions in multiple cancer types. Several microRNAs and genes were found to be associated with tumor survival and progression. Selected target genes were found to be significantly enriched in cancer-related pathways, cancer Hallmark and Gene Ontology (GO) terms. Furthermore, subtype-specific potential gene signatures were discovered in multiple cancer types.

Frequency, cell lineage, and clinical correlates of lymphopenia in untreated T-cell lymphomas (TCL).

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e19023-e19023
Author(s):  
Nathan Denlinger ◽  
Sook (Susan) Hwang ◽  
David Kline ◽  
Eric McLaughlin ◽  
Stephanie Fabbro ◽  
...  
Keyword(s):  
Late Stage ◽  
Lymphocyte Subsets ◽  
Cox Regression ◽  
Early Stage ◽  
Statistical Significance ◽  
Immunosuppressive Treatment ◽  
Advanced Stage ◽  
Newly Diagnosed ◽  
Entire Cohort ◽  
Stage Stage

e19023 Background: Pre-treatment lymphopenia (Lp) independently predicts survival in TCL. However, lack of knowledge about the lineage of the decreased lymphocyte subset(s) prevents hypothesis formulation on pathophysiology of Lp and its effect on survival. Methods: We retrospectively identified 331 adult patients (pts) with biopsy-proven, newly diagnosed TCL between 2001-2016 at OSU. Pts had no prior chemotherapy or immunosuppressive treatment (tx). Clinical data including flow cytometry (FC) at diagnosis were abstracted from the EMR. Lp was defined as absolute lymphocyte count (ALC) <1,000/µL. Cutaneous TCL (CTCL) stage was defined according to modified EORTC/ISLC criteria. Lymphocyte subsets were defined by FC and reported as proportions of ALC. We examined group differences with Fisher’s Exact or the Wilcoxon Rank Sum tests. Survival was analyzed using Cox regression models. Results: Of 331 pts with TCL, 102 were excluded due to prior tx or incomplete data. Of the 229 pts included, 67 had peripheral TCL (PTCL) and 162 had CTCL; 112 mycosis fungoides/sezary syndrome; 99 (61%) early stage (Stage IA-B), 22 (13%) advanced stage (Stage IIB-IVB), and 41 (26%) cutaneous CD30+ lymphoproliferative disorder (CD30+ LPD). Lp was present in 10% of CTCL, 8% in early stage, 22% in late stage, 12% of CD30+LPD, and 34% of PTCL, with overall frequency of 17%. After median follow up of 1,105 days, Lp was associated with worse progression-free survival (PFS) in CTCL pts with an unadjusted hazard ratio of 3.19 (p=0.19). Adjusting for stage and albumin level on multivariate analysis, Lp retained statistical significance for PFS (HR 3.46, p=.04). For the entire cohort, lymphopenia was associated with inferior OS (p=.0027). Analysis of lymphocyte subsets in late stage CTCL compared to early stage showed a significant decrease in B (CD19+) p=.014, NK (CD56+) p=.012, and CD8+ (p=.0045) cell populations. Conclusions: Lp was prevalent (10-34%) in our cohort of newly diagnosed TCL pts and was associated with worse OS. Lp was more prevalent and lymphocyte subsets were significant altered in pts with advanced stage CTCL. These findings will inform further investigations into mechanisms of Lp in TCL, and its potential as predictive biomarker.

scholarly journals Making the Grade during Pandemic: Early-stage and Late-stage Provisional Institutions

2021 ◽  
pp. 073112142110286
Author(s):  
Alexander B. Kinney ◽  
Nicholas J. Rowland
Keyword(s):  
Late Stage ◽  
Early Stage ◽  
State University ◽  
Institutional Work ◽  
Traditional Grading ◽  
Grading Policy ◽  
Temporary Solution ◽  
Rural State ◽  
Empirical Implications ◽  
Common Understanding

This is an article that draws on the institutional work literature about provisional institutions. To date, nearly every U.S. sector has been impacted by COVID-19. To sustain their core missions, highly institutionalized organizations such as universities have had to rethink foundational structures and policies. Using a historical ethnographic approach to investigate records from faculty senate deliberations at “Rural State University” (RSU), the authors examine the implementation of a temporary grading policy to supplement traditional, qualitative grades spring 2020 during the outbreak. The authors find that RSU implemented a temporary, supplemental grading policy as a provisional institution to momentarily supersede traditional grading as a means to—as soon as possible—return to it. This finding contrasts with the common understanding that provisional institutions operate primarily as a temporary solution to a social problem that leads to more stable and enduring, ostensibly nonprovisional institutions. The temporary grading policy, the authors argue, constitutes a “late-stage” provisional institution and, with this new lens, subsequently characterize the more commonplace understanding of provisional institutions as “early-stage.” This contribution has theoretical implications for studies of institutions and empirical implications for research on shared governance and disruption in higher education.

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