Scaling concepts in ‘omics: Nuclear lamin-B scales with tumor growth and often predicts poor prognosis, unlike fibrosis

2021 ◽  
Vol 118 (48) ◽  
pp. e2112940118
Author(s):  
Manasvita Vashisth ◽  
Sangkyun Cho ◽  
Jerome Irianto ◽  
Yuntao Xia ◽  
Mai Wang ◽  
...  
Keyword(s):  
Liver Cancer ◽  
Power Laws ◽  
The Cancer Genome Atlas ◽  
Specific Gene ◽  
Structural Factors ◽  
Lamin B ◽  
Nuclear Lamin ◽  
Survival Gene ◽  
Cancer Genome Atlas ◽  
Tumor Types

Physicochemical principles such as stoichiometry and fractal assembly can give rise to characteristic scaling between components that potentially include coexpressed transcripts. For key structural factors within the nucleus and extracellular matrix, we discover specific gene-gene scaling exponents across many of the 32 tumor types in The Cancer Genome Atlas, and we demonstrate utility in predicting patient survival as well as scaling-informed machine learning (SIML). All tumors with adjacent tissue data show cancer-elevated proliferation genes, with some genes scaling with the nuclear filament LMNB1, including the transcription factor FOXM1 that we show directly regulates LMNB1. SIML shows that such regulated cancers cluster together with longer overall survival than dysregulated cancers, but high LMNB1 and FOXM1 in half of regulated cancers surprisingly predict poor survival, including for liver cancer. COL1A1 is also studied because it too increases in tumors, and a pan-cancer set of fibrosis genes shows substoichiometric scaling with COL1A1 but predicts patient outcome only for liver cancer—unexpectedly being prosurvival. Single-cell RNA-seq data show nontrivial scaling consistent with power laws from bulk RNA and protein analyses, and SIML segregates synthetic from contractile cancer fibroblasts. Our scaling approach thus yields fundamentals-based power laws relatable to survival, gene function, and experiments.

scholarly journals Scaling concepts in 'omics: nuclear lamin-B scales with tumor growth and predicts poor prognosis, whereas fibrosis can be pro-survival

2021 ◽  
Author(s):  
Manasvita Vashisth ◽  
Dennis Discher ◽  
Sangkyun Cho ◽  
Jerome Irianto ◽  
Yuntao Xia ◽  
...  
Keyword(s):  
Single Cell ◽  
Power Laws ◽  
The Cancer Genome Atlas ◽  
Structural Factors ◽  
Rna Seq ◽  
Lamin B ◽  
Nuclear Lamin ◽  
Cancer Genome Atlas ◽  
Tumor Types ◽  
Pan Cancer

Spatiotemporal relationships between genes expressed in tissues likely reflect physicochemical principles that range from stoichiometric interactions to co-organized fractals with characteristic scaling. For key structural factors within the nucleus and extracellular matrix (ECM), gene-gene power laws are found to be characteristic across several tumor types in The Cancer Genome Atlas (TCGA) and across single-cell RNA-seq data. The nuclear filament LMNB1 scales with many tumor-elevated proliferation genes that predict poor survival in liver cancer, and cell line experiments show LMNB1 regulates cancer cell cycle. Also high in the liver, lung, and breast tumors studied here are the main fibrosis-associated collagens, COL1A1 and COL1A2, that scale stoichiometrically with each other and superstoichiometrically with a pan-cancer fibrosis gene set. However, high fibrosis predicts prolonged survival of patients undergoing therapy and does not correlate with LMNB1. Single-cell RNA-seq data also reveal scaling consistent with the pan-cancer power laws obtained from bulk tissue, allowing new power law relations to be predicted. Lastly, although noisy data frustrate weak scaling, concepts such as stoichiometric scaling highlight a simple, internal consistency check to qualify expression data.

scholarly journals Deep Learning for Automatic Subclassification of Gastric Carcinoma Using Whole-Slide Histopathology Images

Cancers ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 3811
Author(s):  
Hyun-Jong Jang ◽  
In-Hye Song ◽  
Sung-Hak Lee
Keyword(s):  
Deep Learning ◽  
Relative Proportion ◽  
The Cancer Genome Atlas ◽  
Cancer Tissue ◽  
Mucinous Tumor ◽  
Receiver Operating Characteristic Curves ◽  
Tumor Tissues ◽  
Cancer Genome Atlas ◽  
Stomach Adenocarcinoma ◽  
Tumor Types

Histomorphologic types of gastric cancer (GC) have significant prognostic values that should be considered during treatment planning. Because the thorough quantitative review of a tissue slide is a laborious task for pathologists, deep learning (DL) can be a useful tool to support pathologic workflow. In the present study, a fully automated approach was applied to distinguish differentiated/undifferentiated and non-mucinous/mucinous tumor types in GC tissue whole-slide images from The Cancer Genome Atlas (TCGA) stomach adenocarcinoma dataset (TCGA-STAD). By classifying small patches of tissue images into differentiated/undifferentiated and non-mucinous/mucinous tumor tissues, the relative proportion of GC tissue subtypes can be easily quantified. Furthermore, the distribution of different tissue subtypes can be clearly visualized. The patch-level areas under the curves for the receiver operating characteristic curves for the differentiated/undifferentiated and non-mucinous/mucinous classifiers were 0.932 and 0.979, respectively. We also validated the classifiers on our own GC datasets and confirmed that the generalizability of the classifiers is excellent. The results indicate that the DL-based tissue classifier could be a useful tool for the quantitative analysis of cancer tissue slides. By combining DL-based classifiers for various molecular and morphologic variations in tissue slides, the heterogeneity of tumor tissues can be unveiled more efficiently.

scholarly journals Novel cancer subtyping method based on patient-specific gene regulatory network

2021 ◽  
Author(s):  
Mai Adachi Nakazawa ◽  
Yoshinori Tamada ◽  
Yoshihisa Tanaka ◽  
Marie Ikeguchi ◽  
Kako Higashihara ◽  
...  
Keyword(s):  
Gene Networks ◽  
Regulatory Networks ◽  
The Cancer Genome Atlas ◽  
Patient Specific ◽  
Specific Gene ◽  
Omics Data ◽  
Cancer Subtypes ◽  
Molecular Systems ◽  
Molecular Features ◽  
Cancer Genome Atlas

The identification of cancer subtypes is important for the understanding of tumor heterogeneity. In recent years, numerous computational methods have been proposed for this problem based on the multi-omics data of patients. It is widely accepted that different cancer subtypes are induced by different molecular regulatory networks. However, only a few incorporate the differences between their molecular systems into the classification processes. In this study, we present a novel method to classify cancer subtypes based on patient-specific molecular systems. Our method quantifies patient-specific gene networks, which are estimated from their transcriptome data. By clustering their quantified networks, our method allows for cancer subtyping, taking into consideration the differences in the molecular systems of patients. Comprehensive analyses of The Cancer Genome Atlas (TCGA) datasets applied to our method confirmed that they were able to identify more clinically meaningful cancer subtypes than the existing subtypes and found that the identified subtypes comprised different molecular features. Our findings show that the proposed method, based on a simple classification using the patient-specific molecular systems, can identify cancer subtypes even with single omics data, which cannot otherwise be captured by existing methods using multi-omics data.

scholarly journals Epstein-Barr Virus-Positive Cancers Show Altered B-Cell Clonality

mSystems ◽  
2018 ◽  
Vol 3 (5) ◽  
Author(s):  
Sara R. Selitsky ◽  
David Marron ◽  
Lisle E. Mose ◽  
Joel S. Parker ◽  
Dirk P. Dittmer
Keyword(s):  
B Cells ◽  
B Cell ◽  
Epstein Barr Virus ◽  
The Cancer Genome Atlas ◽  
Barr Virus ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Epstein Barr ◽  
Tumor Types ◽  
Genome Atlas

ABSTRACTEpstein-Barr virus (EBV) is convincingly associated with gastric cancer, nasopharyngeal carcinoma, and certain lymphomas, but its role in other cancer types remains controversial. To test the hypothesis that there are additional cancer types with high prevalence of EBV, we determined EBV viral expression in all the Cancer Genome Atlas Project (TCGA) mRNA sequencing (mRNA-seq) samples (n= 10,396) from 32 different tumor types. We found that EBV was present in gastric adenocarcinoma and lymphoma, as expected, and was also present in >5% of samples in 10 additional tumor types. For most samples, EBV transcript levels were low, which suggests that EBV was likely present due to infected infiltrating B cells. In order to determine if there was a difference in the B-cell populations, we assembled B-cell receptors for each sample and found B-cell receptor abundance (P≤ 1.4 × 10−20) and diversity (P≤ 8.3 × 10−27) were significantly higher in EBV-positive samples. Moreover, diversity was independent of B-cell abundance, suggesting that the presence of EBV was associated with an increased and altered B-cell population.IMPORTANCEAround 20% of human cancers are associated with viruses. Epstein-Barr virus (EBV) contributes to gastric cancer, nasopharyngeal carcinoma, and certain lymphomas, but its role in other cancer types remains controversial. We assessed the prevalence of EBV in RNA-seq from 32 tumor types in the Cancer Genome Atlas Project (TCGA) and found EBV to be present in >5% of samples in 12 tumor types. EBV infects epithelial cells and B cells and in B cells causes proliferation. We hypothesized that the low expression of EBV in most of the tumor types was due to infiltration of B cells into the tumor. The increase in B-cell abundance and diversity in subjects where EBV was detected in the tumors strengthens this hypothesis. Overall, we found that EBV was associated with an increased and altered immune response. This result is not evidence of causality, but a potential novel biomarker for tumor immune status.

scholarly journals The Gasdermin E gene Potential as a Pan-Cancer Biomarker, While Discriminating between Different Tumor Types

Cancers ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1810 ◽  
Author(s):  
Joe Ibrahim ◽  
Ken Op de Beeck ◽  
Erik Fransen ◽  
Marc Peeters ◽  
Guy Van Camp
Keyword(s):  
The Cancer Genome Atlas ◽  
Tumor Type ◽  
E Gene ◽  
Normal Tissues ◽  
Incidence And Mortality ◽  
Cancer Genome Atlas ◽  
Cancer Setting ◽  
Tumor Types ◽  
Methylation Patterns ◽  
Pan Cancer

Due to the elevated rates of incidence and mortality of cancer, early and accurate detection is crucial for achieving optimal treatment. Molecular biomarkers remain important screening and detection tools, especially in light of novel blood-based assays. DNA methylation in cancer has been linked to tumorigenesis, but its value as a biomarker has not been fully explored. In this study, we have investigated the methylation patterns of the Gasdermin E gene across 14 different tumor types using The Cancer Genome Atlas (TCGA) methylation data (N = 6502). We were able to identify six CpG sites that could effectively distinguish tumors from normal samples in a pan-cancer setting (AUC = 0.86). This combination of pan-cancer biomarkers was validated in six independent datasets (AUC = 0.84–0.97). Moreover, we tested 74,613 different combinations of six CpG probes, where we identified tumor-specific signatures that could differentiate one tumor type versus all the others (AUC = 0.79–0.98). In all, methylation patterns exhibited great variation between cancer and normal tissues, but were also tumor specific. Our analyses highlight that a Gasdermin E methylation biomarker assay, not only has the potential for being a methylation-specific pan-cancer detection marker, but it also possesses the capacity to discriminate between different types of tumors.

scholarly journals The Prediction and Prognostic Significance of INPP5K Expression in Patients with Liver Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Ruobing Wang ◽  
Yan Jiao ◽  
Yanqing Li ◽  
Siyang Ye ◽  
Guoqiang Pan ◽  
...  
Keyword(s):  
Liver Cancer ◽  
Mrna Expression ◽  
Poor Prognosis ◽  
Prognostic Significance ◽  
The Cancer Genome Atlas ◽  
Inositol Polyphosphate ◽  
Chi Square ◽  
Cancer Genome Atlas ◽  
Poor Outcomes ◽  
The Relationship

Liver cancer is a devastating disease for humans with poor prognosis. Although the survival rate of patients with liver cancer has improved in the past decades, the recurrence and metastasis of liver cancer are still obstacles for us. Inositol polyphosphate-5-phosphatase K (INPP5K) belongs to the family of phosphoinositide 5-phosphatases (PI 5-phosphatases), which have been reported to be associated with cell migration, polarity, adhesion, and cell invasion, especially in cancers. However, there have been few studies on the correlation of INPP5K and liver cancer. In this study, we explored the prognostic significance of INPP5K in liver cancer through bioinformatics analysis of data collected from The Cancer Genome Atlas (TCGA) database. Chi-square and Fisher exact tests were used to evaluate the relationship between INPP5K expression and clinical characteristics. Our results showed that low INPP5K expression was correlated with poor outcomes in liver cancer patients. Univariate and multivariate Cox analyses demonstrated that low INPP5K mRNA expression played a significant role in shortening overall survival (OS) and relapse-free survival (RFS), which might serve as the useful biomarker and prognostic factor for liver cancer. In conclusion, low INPP5K mRNA expression is an independent risk factor for poor prognosis in liver cancer.

scholarly journals Mast Cells Resting-Related Prognostic Signature in Hepatocellular Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hao Zhang ◽  
Lin Sun ◽  
Xiao Hu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Mast Cells ◽  
Liver Cancer ◽  
Differentially Expressed Genes ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Immune Checkpoints ◽  
New Ideas ◽  
Cancer Genome Atlas

The immune microenvironment of liver cancer is of great significance for the treatment of liver cancer. After evaluating the content of mast cells resting in the transcriptome data of The Cancer Genome Atlas database by CIBERSORT analysis, this study aimed to group the samples according to the content of mast cells resting in different samples to find the differentially expressed genes in the two groups. Significant prognostic differences were found between high and low mast cells resting infiltration groups. The prognostic model was constructed according to the differentially expressed genes. The model was validated using external independent datasets. The results revealed that the constructed model was reliable. It could well distinguish the prognostic differences of patients in different characteristic groups. The high-risk group was mainly concentrated in metabolic pathways. The risk score of this model was closely related to some immune cells, immune function, and immune checkpoints. Therefore, this model may provide new ideas for immunotherapy of hepatocellular carcinoma.

scholarly journals Computational analysis of pathological image enables interpretable prediction for microsatellite instability

2020 ◽  
Author(s):  
Jin Zhu ◽  
Wangwei Wu ◽  
Yuting Zhang ◽  
Shiyun Lin ◽  
Yukang Jiang ◽  
...  
Keyword(s):  
Microsatellite Instability ◽  
The Cancer Genome Atlas ◽  
Patient Treatment ◽  
Classification Framework ◽  
Texture Characteristics ◽  
Cancer Genome Atlas ◽  
Tumor Types ◽  
Deep Learning Model ◽  
Pathological Image ◽  
Interpretable Classification

AbstractObjectiveMicrosatellite instability (MSI) is associated with several tumor types and its status has become increasingly vital in guiding patient treatment decisions. However, in clinical practice, distinguishing MSI from its counterpart is challenging since the diagnosis of MSI requires additional genetic or immunohistochemical tests. In this study, we aimed to establishe an interpretable pathological image analysis strategies to help medical experts to identify MSI automatically.DesignThree cohorts of Haematoxylin and eosin-stained whole-slide images from 1033 patients with different tumor types were collected from The Cancer Genome Atlas. These images were preprocessed and tessallated into small tiles. A image-level interpretable deep learning model and a feature-level interpretable random forest model were built up on these files.ResultsBoth models performed well in the three datasets and achieved image-level and feature-level interpretability repectively. Importantly, both from the image-level and feature-level interpretability, color features and texture characteristics are shown to contribute the most to the MSI prediction. Based on them, we established an interpretable classification framework. Therefore, the classification models under the proposed framework can serve as an efficient tool for predicting the MSI status of patients.ConclusionThis study establishes a interpretable classification framework to for predicting the MSI status of patients and provide more insights to pathologists with clinical understanding.

scholarly journals Novel Insights into Epigenetic Regulation of IL6 Pathway: In Silico Perspective on Inflammation and Cancer Relationship

2021 ◽  
Vol 22 (18) ◽  
pp. 10172
Author(s):  
Saverio Candido ◽  
Barbara Maria Rita Tomasello ◽  
Alessandro Lavoro ◽  
Luca Falzone ◽  
Giuseppe Gattuso ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Epigenetic Regulation ◽  
Computational Analysis ◽  
The Cancer Genome Atlas ◽  
Post Translational Modifications ◽  
Pathway Gene ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Tumor Types

IL-6 pathway is abnormally hyperactivated in several cancers triggering tumor cell growth and immune system inhibition. Along with genomic mutation, the IL6 pathway gene expression can be affected by DNA methylation, microRNAs, and post-translational modifications. Computational analysis was performed on the Cancer Genome Atlas (TCGA) datasets to explore the role of IL6, IL6R, IL6ST, and IL6R transmembrane isoform expression and their epigenetic regulation in different cancer types. IL6 was significantly modulated in 70% of tumor types, revealing either up- or down-regulation in an approximately equal number of tumors. Furthermore, IL6R and IL6ST were downregulated in more than 10 tumors. Interestingly, the correlation analysis demonstrated that only the IL6R expression was negatively affected by the DNA methylation within the promoter region in most tumors. Meanwhile, only the IL6ST expression was extensively modulated by miRNAs including miR-182-5p, which also directly targeted all three genes. In addition, IL6 upregulated miR-181a-3p, mirR-214-3p, miR-18a-5p, and miR-938, which in turn inhibited the expression of IL6 receptors. Finally, the patients’ survival rate was significantly affected by analyzed targets in some tumors. Our results suggest the relevance of epigenetic regulation of IL6 signaling and pave the way for further studies to validate these findings and to assess the prognostic and therapeutic predictive value of these epigenetic markers on the clinical outcome and survival of cancer patients.

scholarly journals Inferring Homologous Recombination Deficiency of Ovarian Cancer From the Landscape of Copy Number Variation at Subchromosomal and Genetic Resolutions

2021 ◽  
Vol 11 ◽  
Author(s):  
Meng Zhang ◽  
Si-Cong Ma ◽  
Jia-Le Tan ◽  
Jian Wang ◽  
Xue Bai ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Homologous Recombination ◽  
Copy Number Variation ◽  
Copy Number ◽  
The Cancer Genome Atlas ◽  
Homologous Recombination Deficiency ◽  
Number Variation ◽  
Cancer Genome Atlas ◽  
Tumor Types ◽  
Pan Cancer

BackgroundHomologous recombination deficiency (HRD) is characterized by overall genomic instability and has emerged as an indispensable therapeutic target across various tumor types, particularly in ovarian cancer (OV). Unfortunately, current detection assays are far from perfect for identifying every HRD patient. The purpose of this study was to infer HRD from the landscape of copy number variation (CNV).MethodsGenome-wide CNV landscape was measured in OV patients from the Australian Ovarian Cancer Study (AOCS) clinical cohort and >10,000 patients across 33 tumor types from The Cancer Genome Atlas (TCGA). HRD-predictive CNVs at subchromosomal resolution were identified through exploratory analysis depicting the CNV landscape of HRD versus non-HRD OV patients and independently validated using TCGA and AOCS cohorts. Gene-level CNVs were further analyzed to explore their potential predictive significance for HRD across tumor types at genetic resolution.ResultsAt subchromosomal resolution, 8q24.2 amplification and 5q13.2 deletion were predominantly witnessed in HRD patients (both p < 0.0001), whereas 19q12 amplification occurred mainly in non-HRD patients (p < 0.0001), compared with their corresponding counterparts within TCGA-OV. The predictive significance of 8q24.2 amplification (p < 0.0001), 5q13.2 deletion (p = 0.0056), and 19q12 amplification (p = 0.0034) was externally validated within AOCS. Remarkably, pan-cancer analysis confirmed a cross-tumor predictive role of 8q24.2 amplification for HRD (p < 0.0001). Further analysis of CNV in 8q24.2 at genetic resolution revealed that amplifications of the oncogenes, MYC (p = 0.0001) and NDRG1 (p = 0.0004), located on this fragment were also associated with HRD in a pan-cancer manner.ConclusionsThe CNV landscape serves as a generalized predictor of HRD in cancer patients not limited to OV. The detection of CNV at subchromosomal or genetic resolution could aid in the personalized treatment of HRD patients.

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