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 A Comprehensive Survey of Immune Cytolytic Activity-Associated Gene Co-Expression Networks across 17 Tumor and Normal Tissue Types

Cancers ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 307
Author(s):  
Tian Tian ◽  
Ji Wan ◽  
Yan Han ◽  
Haoran Liu ◽  
Feng Gao ◽  
...  
Keyword(s):  
Cytolytic Activity ◽  
The Cancer Genome Atlas ◽  
Immune Activity ◽  
Multiple Tumor ◽  
Normal Tissues ◽  
Tumor Tissues ◽  
Comprehensive Survey ◽  
Cancer Genome Atlas ◽  
Normal Networks ◽  
Tumor Types

Cytolytic immune activity in solid tissue can be quantified by transcript levels of two genes, GZMA and PRF1, which is named the CYT score. A previous study has investigated the molecular and genetic properties of tumors associated CYT, but a systematic exploration of how co-expression networks across different tumors are shaped by anti-tumor immunity is lacking. Here, we examined the connectivity and biological themes of CYT-associated modules in gene co-expression networks of 14 tumor and 3 matched normal tissues constructed from the RNA-Seq data of the “The Cancer Genome Atlas” project. We first found that tumors networks have more diverse CYT-correlated modules than normal networks. We next identified and investigated tissue-specific CYT-associated modules across 14 tumor types. Finally, a common CYT-associated network across 14 tumor types was constructed. Two common modules have mixed signs of correlation with CYT in different tumors. Given the tumors and normal tissues surveyed, our study presents a systematic view of the regulation of cytolytic immune activity across multiple tumor tissues.

scholarly journals Potential Prognostic Value and Mechanism of Stromal-Immune Signature in Tumor Microenvironment for Stomach Adenocarcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Xinying Zhu ◽  
Xiaoli Xie ◽  
Qingchao Zhao ◽  
Lixian Zhang ◽  
Changjuan Li ◽  
...  
Keyword(s):  
Immune Cells ◽  
The Cancer Genome Atlas ◽  
Tumor Differentiation ◽  
Coexpression Analysis ◽  
Immune Signature ◽  
Rna Transcripts ◽  
Tumor Tissues ◽  
Cancer Genome Atlas ◽  
Stomach Adenocarcinoma ◽  
Stromal Signature

Stomach adenocarcinoma (STAD) is one of the most common malignancies. But the molecular mechanism is unknown. In this study, we downloaded the transcriptional profiles and clinical data of 344 STAD and 30 normal samples from The Cancer Genome Atlas (TCGA) database. Stromal and immune scores of STAD were calculated by the Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm, and association of stromal/immune scores with tumor differentiation/T/N/M stage and survival was investigated. The differentially expressed genes (DEGs) between high and low score groups (based on media) were identified, and prognostic genes over-/underexpressed in both STAD and stromal/immune signature were retrieved. Furthermore, proportions of 22 infiltrating immune cells for the cohort from TCGA were estimated by the Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) algorithm, and association of 22 immune cells with tumor differentiation/T/N/M stage and survival was investigated. Next, coexpression analysis of 22 immune cells and intersection genes over-/underexpressed in both STAD and stromal signature was conducted. We found high stromal and immune scores and macrophage infiltration predicting poor tumor differentiation and severe local invasion, obtained a list of prognostic genes based on stromal-immune signature, and explored the interaction of collagen, chemokines such as CXCL9, CXCL10, and CXCL11, and macrophage through coexpression analysis and may provide novel prognostic biomarkers and immunotherapeutic targets for STAD.

scholarly journals Pan-cancer analysis of transcripts encoding novel open-reading frames (nORFs) and their potential biological functions

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Chaitanya Erady ◽  
Adam Boxall ◽  
Shraddha Puntambekar ◽  
N. Suhas Jagannathan ◽  
Ruchi Chauhan ◽  
...  
Keyword(s):  
Transcript Level ◽  
Open Reading Frames ◽  
The Cancer Genome Atlas ◽  
Small Subset ◽  
Cancer Tissue ◽  
Post Translational Modifications ◽  
Cancer Genome Atlas ◽  
Systematic Identification ◽  
Reading Frames

AbstractUncharacterized and unannotated open-reading frames, which we refer to as novel open reading frames (nORFs), may sometimes encode peptides that remain unexplored for novel therapeutic opportunities. To our knowledge, no systematic identification and characterization of transcripts encoding nORFs or their translation products in cancer, or in any other physiological process has been performed. We use our curated nORFs database (nORFs.org), together with RNA-Seq data from The Cancer Genome Atlas (TCGA) and Genotype-Expression (GTEx) consortiums, to identify transcripts containing nORFs that are expressed frequently in cancer or matched normal tissue across 22 cancer types. We show nORFs are subject to extensive dysregulation at the transcript level in cancer tissue and that a small subset of nORFs are associated with overall patient survival, suggesting that nORFs may have prognostic value. We also show that nORF products can form protein-like structures with post-translational modifications. Finally, we perform in silico screening for inhibitors against nORF-encoded proteins that are disrupted in stomach and esophageal cancer, showing that they can potentially be targeted by inhibitors. We hope this work will guide and motivate future studies that perform in-depth characterization of nORF functions in cancer and other diseases.

scholarly journals MEX3A and MEX3B as Potential Prognostic Indicators in Stomach Adenocarcinoma

2021 ◽  
Author(s):  
Junwei Zou ◽  
Yong Huang ◽  
Zhaoying Wu ◽  
Hao Xie ◽  
Rongsheng Wang ◽  
...  
Keyword(s):  
Overall Survival ◽  
Rna Splicing ◽  
Rna Binding ◽  
The Cancer Genome Atlas ◽  
Prognostic Indicators ◽  
Kaplan Meier ◽  
Cancer Genome Atlas ◽  
Stomach Adenocarcinoma ◽  
Tumor Pathogenesis ◽  
Kaplan Meier Plotter

Abstract Stomach adenocarcinoma(STAD) is one of the deadliest cancers in the world. The expression levels of family members of mex-3 RNA that bound MEX3A (member A) and MEX3B (member B) were high expressions in different cancers and interconnected to deficient prognosis. The present research assessed the potential regarding the expression of MEX3A and MEX3B in STAD by analysing the facts of STAD (viz. The Cancer Genome Atlas). TCGA, MEX3A and MEX3B in the cancers were analyzed using TIMER2.0, Kaplan Meier Plotter, and cBioPortal. The data was visualized using version 4.0.3 of R. We found MEX3A and MEX3B had various expressions regarding major cancer and relevant common tissues. Especially, high expression of MEX3A and MEX3B had relationships with the OS (namely overall survival) with deficiency and RFS (viz. relapse-free survival) concerning STAD. The expressions of MEX3B had correlations to T stage with P being 0.012 and to the race with P being 0.049. MEX3B was highly expressed in T3 and T4 stages, and was highly expressed in the white race. MEX3A mutation had a better survival without diseases, with P being 0.0205. However, the situation was different with non-overall survival, with P being 0.194, in comparison with the patients who did not have MEX3A change. MEX3A and MEX3B on tumor pathogenesis might be related to "RNA splicing" and "spliceosomal complex" and "single-stranded RNA binding". We further investigated the association between MEX3A and MEX3B and immune cells. The mast cells of the most connections to MEX3A (R=-0.300, P<0.001) and the NK cells were positively correlation with MEX3B (R=0.590, P<0.001). It showed that they might be potential prognostic molecular biomarkers in patients with STAD.

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.

Heterogeneity of MSI-H gastric cancer identifies a subtype with worse survival

2020 ◽  
Vol 58 (1) ◽  
pp. 12-19 ◽  
Author(s):  
Yanmei Yang ◽  
Zhong Shi ◽  
Rui Bai ◽  
Wangxiong Hu
Keyword(s):  
Immune Checkpoint Blockade ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Synonymous Mutations ◽  
The Poor ◽  
Symptom Alleviation ◽  
Cancer Genome Atlas ◽  
Stomach Adenocarcinoma ◽  
Asian Cohort ◽  
Matrix Factorisation

BackgroundMicrosatellite instability-high (MSI-H) tumour patients generally have a better prognosis than microsatellite-stable (MSS) ones due to the large number of non-synonymous mutations. However, an increasing number of studies have revealed that less than half of MSI-H patients gain survival benefits or symptom alleviation from immune checkpoint-blockade treatment. Thus, an in-depth inspection of heterogeneous MSI-H tumours is urgently required.MethodsHere, we used non-negative matrix factorisation (non-NMF)-based consensus clustering to define stomach adenocarcinoma (STAD) MSI-H subtypes in samples from The Cancer Genome Atlas and an Asian cohort, GSE62254.ResultsMSI-H STAD samples are basically clustered into two subgroups (MSI-H1 and MSI-H2). Further examination of the immune landscape showed that immune suppression factors were enriched in the MSI-H1 subgroup, which may be associated with the poor prognosis in this subgroup.ConclusionsOur results illustrate the genetic heterogeneity within MSI-H STADs, with important implications for cancer patient risk stratification, prognosis and treatment.

scholarly journals Deciphering N6-Methyladenosine-Related Genes Signature to Predict Survival in Lung Adenocarcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Jie Zhu ◽  
Min Wang ◽  
Daixing Hu
Keyword(s):  
Lung Adenocarcinoma ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Lasso Regression ◽  
Gene Expressions ◽  
Tumor Tissues ◽  
Cancer Genome Atlas ◽  
Selection Operator ◽  
Cox Analysis

Lung cancer is the most commonly diagnosed cancer and the leading cause of cancer-related death. Among these, lung adenocarcinoma (LUAD) accounts for most cases. Due to the improvement of precision medicine based on molecular characterization, the treatment of LUAD underwent significant changes. With these changes, the prognosis of LUAD becomes diverse. N6-methyladenosine (m6A) is the most predominant modification in mRNAs, which has been a research hotspot in the field of oncology. Nevertheless, little has been studied to reveal the correlations between the m6A-related genes and prognosis in LUAD. Thus, we conducted a comprehensive analysis of m6A-related gene expressions in LUAD patients based on The Cancer Genome Atlas (TCGA) database by revealing their relationship with prognosis. Different expressions of the m6A-related genes in tumor tissues and non-tumor tissues were confirmed. Furthermore, their relationship with prognosis was studied via Consensus Clustering Analysis, Principal Components Analysis (PCA), and Least Absolute Shrinkage and Selection Operator (LASSO) Regression. Based on the above analyses, a m6A-based signature to predict the overall survival (OS) in LUAD was successfully established. Among the 479 cases, we found that most of the m6A-related genes were differentially expressed between tumor and non-tumor tissues. Six genes, HNRNPC, METTL3, YTHDC2, KIAA1429, ALKBH5, and YTHDF1 were screened to build a risk scoring signature, which is strongly related to the clinical features pathological stages (p<0.05), M stages (p<0.05), T stages (p < 0.05), gender (p=0.04), and survival outcome (p=0.02). Multivariate Cox analysis indicated that risk value could be used as an independent prognostic factor, revealing that the m6A-related genes signature has great predictive value. Its efficacy was also validated by data from the Gene Expression Omnibus (GEO) database.

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 Elevated ZNF703 Protein Expression Is an Independent Unfavorable Prognostic Factor for Survival of the Patients with Head and Neck Squamous Cell Carcinoma

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Hang Yang ◽  
Wen-Qi Jiang ◽  
Ye Cao ◽  
Yong-An Sun ◽  
Jing Wei ◽  
...  
Keyword(s):  
Prognostic Factor ◽  
Head And Neck ◽  
Squamous Cell ◽  
The Cancer Genome Atlas ◽  
Cancer Center ◽  
Protein Overexpression ◽  
Prognostic Effect ◽  
Tumor Tissues ◽  
Cancer Genome Atlas

Aim. Data from The Cancer Genome Atlas (TCGA) show that the ZNF703 gene amplifies and overexpresses in head and neck squamous cell carcinomas (HNSCC). However, the clinical relevance of this observation in HNSCC is unclear. The purpose of this study was to clarify the expression of ZNF703 protein and its prognostic effect on HNSCC.Methods. Two hundred ten HNSCC patients from Sun Yat-Sen University Cancer Center with complete survival follow-up were included in this study. Tumor samples from primary sites were collected. The expression of the ZNF703 protein was tested by immunohistochemistry (IHC).Results. The high expression of ZNF703 in HNSCC tumor tissues was significantly higher than that of the matched noncancerous tissues (48.6% versus 11.6%,P<0.001). ZNF703 overexpression was correlated with tumor position (laryngeal carcinoma) and recurrence (allP<0.05). Multivariate analysis revealed that ZNF703 protein overexpression was an independent prognostic factor (P=0.022, hazard ratio = 1.635, 95% CI 1.073–2.493) in HNSCC patients.Conclusion. ZNF703 overexpression is associated with adverse prognosis in HNSCC, which might be a novel biomarker of HNSCC.

Sign in / Sign up

Export Citation Format

Share Document