scholarly journals Computational staining of pathology images to study tumor microenvironment in lung cancer

2019 ◽  
Author(s):  
Shidan Wang ◽  
Ruichen Rong ◽  
Donghan M. Yang ◽  
Ling Cai ◽  
Lin Yang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Tumor Microenvironment ◽  
Spatial Organization ◽  
Risk Group ◽  
Biological Pathways ◽  
The Cancer Genome Atlas ◽  
Analysis Tool ◽  
Cell Nuclei ◽  
Tumor Tissues ◽  
Different Types

ABSTRACTThe spatial organization of different types of cells in tumor tissues reveals important information about the tumor microenvironment (TME). In order to facilitate the study of cellular spatial organization and interactions, we developed a comprehensive nuclei segmentation and classification tool to characterize the TME from standard Hematoxylin and Eosin (H&E)-stained pathology images. This tool can computationally “stain” different types of cell nuclei in H&E pathology images to facilitate pathologists in analyzing the TME.A Mask Regional-Convolutional Neural Network (Mask-RCNN) model was developed to segment the nuclei of tumor, stromal, lymphocyte, macrophage, karyorrhexis and red blood cells in lung adenocarcinoma (ADC). Using this tool, we identified and classified cell nuclei and extracted 48 cell spatial organization-related features that characterize the TME. Using these features, we developed a prognostic model from the National Lung Screening Trial dataset, and independently validated the model in The Cancer Genome Atlas (TCGA) lung ADC dataset, in which the predicted high-risk group showed significantly worse survival than the low-risk group (pv= 0.001), with a hazard ratio of 2.23 [1.37-3.65] after adjusting for clinical variables. Furthermore, the image-derived TME features were significantly correlated with the gene expression of biological pathways. For example, transcription activation of both the T-cell receptor (TCR) and Programmed cell death protein 1 (PD1) pathways was positively correlated with the density of detected lymphocytes in tumor tissues, while expression of the extracellular matrix organization pathway was positively correlated with the density of stromal cells.This study developed a deep learning-based analysis tool to dissect the TME from tumor tissue images. Using this tool, we demonstrated that the spatial organization of different cell types is predictive of patient survival and associated with the gene expression of biological pathways. Although developed from the pathology images of lung ADC, this model can be adapted into other types of cancers.

scholarly journals Data mining of immune-related prognostic genes in metastatic melanoma microenvironment

2020 ◽  
Vol 40 (11) ◽  
Author(s):  
Wei Han ◽  
Biao Huang ◽  
Xiao-Yu Zhao ◽  
Guo-Liang Shen
Keyword(s):  
Gene Expression ◽  
Tumor Microenvironment ◽  
Metastatic Melanoma ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Protein Protein Interaction ◽  
Subtype Identification ◽  
Cancer Genome Atlas ◽  
Immune Related Genes

Abstract Skin cutaneous melanoma (SKCM) is one of the most deadly malignancies. Although immunotherapies showed the potential to improve the prognosis for metastatic melanoma patients, only a small group of patients can benefit from it. Therefore, it is urgent to investigate the tumor microenvironment in melanoma as well as to identify efficient biomarkers in the diagnosis and treatments of SKCM patients. A comprehensive analysis was performed based on metastatic melanoma samples from the Cancer Genome Atlas (TCGA) database and ESTIMATE algorithm, including gene expression, immune and stromal scores, prognostic immune-related genes, infiltrating immune cells analysis and immune subtype identification. Then, the differentially expressed genes (DEGs) were obtained based on the immune and stromal scores, and a list of prognostic immune-related genes was identified. Functional analysis and the protein–protein interaction network revealed that these genes enriched in multiple immune-related biological processes. Furthermore, prognostic genes were verified in the Gene Expression Omnibus (GEO) databases and used to predict immune infiltrating cells component. Our study revealed seven immune subtypes with different risk values and identified T cells as the most abundant cells in the immune microenvironment and closely associated with prognostic outcomes. In conclusion, the present study thoroughly analyzed the tumor microenvironment and identified prognostic immune-related biomarkers for metastatic melanoma.

scholarly journals Mapping Tumor-Specific Expression QTLs in Impure Tumor Samples

2017 ◽  
Author(s):  
Douglas R. Wilson ◽  
Wei Sun ◽  
Joseph G. Ibrahim
Keyword(s):  
Gene Expression ◽  
Type I Error ◽  
The Cancer Genome Atlas ◽  
Type I ◽  
Eqtl Mapping ◽  
Rna Seq ◽  
Specific Expression ◽  
Normal Cells ◽  
Technology Application ◽  
Tumor Tissues

AbstractThe study of gene expression quantitative trait loci (eQTL) is an effective approach to illuminate the functional roles of genetic variants. Computational methods have been developed for eQTL mapping using gene expression data from microarray or RNA-seq technology. Application of these methods for eQTL mapping in tumor tissues is problematic because tumor tissues are composed of both tumor and infiltrating normal cells (e.g. immune cells) and eQTL effects may vary between tumor and infiltrating normal cells. To address this challenge, we have developed a new method for eQTL mapping using RNA-seq data from tumor samples. Our method separately estimates the eQTL effects in tumor and infiltrating normal cells using both total expression and allele-specific expression (ASE). We demonstrate that our method controls type I error rate and has higher power than some alternative approaches. We applied our method to study RNA-seq data from The Cancer Genome Atlas and illustrated the similarities and differences of eQTL effects in tumor and normal cells.

scholarly journals Genome Based Search for Reference Genes for Gene Expression Analysis in Oral Cancer: a Data Science Driven Approach

2019 ◽  
Author(s):  
Nehanjali Dwivedi ◽  
Sujan K Dhar ◽  
G Charitha ◽  
Moni Abraham Kuriakose ◽  
Amritha Suresh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Expression Analysis ◽  
Reference Genes ◽  
Data Science ◽  
Gene Expression Analysis ◽  
The Cancer Genome Atlas ◽  
Experimental Conditions ◽  
Statistical Measures ◽  
Different Types

Abstract Background Quantitative real time PCR (qPCR) remains by far the most cost-effective, fast yet sensitive technique to check the gene expression levels in various systems. The traditionally used reference genes over the years were found to be regulated heavily based on sample sources and/or experimental conditions. This paper therefore presents a data science driven -omic approach for selection of reference genes from ~60,000 candidates from The Cancer Genome Atlas (TCGA) and Broad Institute Cancer Cell Line Encyclopaedia (CCLE) for gene expression studies in head and neck squamous cell carcinoma (HNSCC). mRNA-sequencing data of 500 patient samples and 33 cell lines from publicly available databases were analysed to assess stability of genes in terms of multiple statistical measures. A final set of 12 candidate genes were studied in the Indian set of data in Gene Expression Omnibus (GEO) and validated experimentally using qPCR in 35 different types of samples from platforms like drug sensitive and resistant cell lines, normal and tumor samples, fibroblast and epithelial primary culture derived from HNSCC patients from India. Result The study lead to the choice of five most stable reference genes –TYW5, RIC8B, PLEKHA3, CEP57L1 and GPR89B across three experimental platforms. Conclusion In addition to providing a set of five most stable reference genes for future gene expression analysis experiments across different types of samples in HNSCC, the study also presents an objective framework for assessing reference genes for other disease areas as well.

scholarly journals Identification of Distinct Molecular Patterns and a Four-Gene Signature in Colon Cancer Based on Invasion-Related Genes

2021 ◽  
Vol 12 ◽  
Author(s):  
Yunfei Dong ◽  
Tao Shang ◽  
HaiXin Ji ◽  
Xiukou Zhou ◽  
Zhi Chen
Keyword(s):  
Gene Expression ◽  
Colon Cancer ◽  
Cancer Patients ◽  
Risk Group ◽  
Univariate Analysis ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Training Dataset ◽  
Lasso Regression ◽  
Independent Predictive Factor

BackgroundThe pathological stage of colon cancer cannot accurately predict recurrence, and to date, no gene expression characteristics have been demonstrated to be reliable for prognostic stratification in clinical practice, perhaps because colon cancer is a heterogeneous disease. The purpose was to establish a comprehensive molecular classification and prognostic marker for colon cancer based on invasion-related expression profiling.MethodsFrom the Gene Expression Omnibus (GEO) database, we collected two microarray datasets of colon cancer samples, and another dataset was obtained from The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) further underwent univariate analysis, least absolute shrinkage, selection operator (LASSO) regression analysis, and multivariate Cox survival analysis to screen prognosis-associated feature genes, which were further verified with test datasets.ResultsTwo molecular subtypes (C1 and C2) were identified based on invasion-related genes in the colon cancer samples in TCGA training dataset, and C2 had a good prognosis. Moreover, C1 was more sensitive to immunotherapy. A total of 1,514 invasion-related genes, specifically 124 downregulated genes and 1,390 upregulated genes in C1 and C2, were identified as DEGs. A four-gene prognostic signature was identified and validated, and colon cancer patients were stratified into a high-risk group and a low-risk group. Multivariate regression analyses and a nomogram indicated that the four-gene signature developed in this study was an independent predictive factor and had a relatively good predictive capability when adjusting for other clinical factors.ConclusionThis research provided novel insights into the mechanisms underlying invasion and offered a novel biomarker of a poor prognosis in colon cancer patients.

scholarly journals Comprehensive analysis of the significance of ferroptosis-related genes in the prognosis and immunotherapy of oral squamous cell carcinoma

2020 ◽  
Author(s):  
Junhao Yin ◽  
Xiaoli Zeng ◽  
Zexin Ai ◽  
Miao Yu ◽  
Yang'ou Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Overall Survival ◽  
Squamous Cell Carcinoma ◽  
High Risk ◽  
Tumor Microenvironment ◽  
Oral Squamous Cell Carcinoma ◽  
Cox Regression ◽  
Risk Group ◽  
Low Risk ◽  
Regression Analyses

Abstract Background: Oral squamous cell carcinoma (OSCC) is a life-threatening disease that emerged as a major international health concern, associated with poor prognosis and the absence of specific biomarkers. Studies have shown that the ferroptosis-related genes (FRGs) can be used as tumor prognostic markers. However, FRGs’ prognostic value in OSCC needs further exploration. Our aim was to construct a novel FRG signature for overall survival (OS) prediction in OSCC patients and explore its role in immunotherapy.Methods: In our study, gene expression profile and clinical data of OSCC patients were collected from a public domain. FRGs were available from the FerrDb database. We performed univariate and multivariate Cox regression analyses to construct a multigene signature. The Kaplan-Meier (K-M) and receiver operating characteristic (ROC) methods were utilized to test the effectiveness of the FRG signature. A differential gene expression analysis was performed by the limma R package, followed by functional enrichment analyses. CIBERSORT was applied to analyze the tumor microenvironment (TME). Finally, the expression of human leukocyte antigen (HLA) and immune checkpoint molecules were analyzed to confirm the sensitivity of immunotherapy.Results: A total of 103 FRGs, expressed in OSCC (FRGs-OSCC), were identified from the two datasets by the Venn analysis. The Cox regression analysis identified 5 FRGs-OSCC that were associated with overall survival (all P < 0.01). The FRGs-OSCC risk model was established to classify patients into high risk and low risk groups. Compared with the low risk group, the survival time of the high-risk group was significantly reduced (P < 0.001). According to the multivariate Cox regression analyses, the risk score acted as an independent predictor for OS (HR > 1, P < 0.001). The accuracy of the FRGs-OSCC risk predictive model was confirmed by ROC curve analysis. The results of the Kyoto Encyclopedia of Genes and Genomes (KEGG) showed significant enrichment of immune-related pathways, and a difference in tumor microenvironment between the two groups. The low risk group had the characteristics of higher expression of HLA and immune checkpoints (IDO1, LAG3, PDCD1 and TIGHT), a lower tumor purity and a higher infiltration of immune cells, indicating a more sensitive response to immunotherapy.Conclusions: The novel FRGs-OSCC risk score system can be used to predict OSCC prognosis. Ferroptosis targeting may be a therapeutic option for OSCC.

scholarly journals Comprehensive analysis of PD-L1 expression, tumor-infiltrating lymphocytes, and tumor microenvironment in LUAD: differences between Asians and Caucasians

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Fenglong Bie ◽  
He Tian ◽  
Nan Sun ◽  
Ruochuan Zang ◽  
Moyan Zhang ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Tumor Infiltrating Lymphocytes ◽  
The Cancer Genome Atlas ◽  
Normal Tissues ◽  
Asian Patients ◽  
Expression Of Genes ◽  
Tumor Tissues ◽  
Significant Difference ◽  
Cancer Genome Atlas ◽  
Infiltrating Lymphocytes

Abstract Backgrounds The characteristics of programmed cell death protein-1 (PD-L1) expression, tumor-infiltrating lymphocytes (TILs), and tumor microenvironment (TME) in lung adenocarcinoma (LUAD) patients are closely related to immunotherapy, and there are differences between Asians and Caucasians. Methods Acquire the transcriptome data of the Cancer Genome Atlas and Chinese LUAD patients. R software was used to analyze the differential expression of genes, prognosis, and gene function. Use CIBERSORT for TIL-related analysis and ESTIMATE for TME-related analysis. Results The expression of PD-L1 in tumor tissues of Caucasian LUAD patients was lower than that in normal tissues, while there was no significant difference in Asians. There was no statistical difference between PD-L1 expression and prognosis. The composition of TILs between Caucasian and Asian LUAD patients was quite different. There was no correlation between TILs and prognosis in Caucasians. However, the higher content of resting mast cells indicated a better prognosis in Asians. The Caucasian patients with higher immune and estimate scores had a better prognosis (p = 0.021, p = 0.025). However, the Asian patients with a higher estimate score had a worse prognosis (p = 0.024). The high expression of COL5A2 (p = 0.046, p = 0.027) and NOX4 (p = 0.020, p = 0.019) were both associated with the poor prognosis in Caucasians and Asians. Conclusion There are many differences in the characteristics of PD-L1 expression, TILs, and TME between Caucasian and Asian LUAD patients. This provides a certain hint for the selection of specific immunotherapy strategies separately for Caucasian and Asian LUAD patients.

scholarly journals The Multifaceted Role of Connexins in Tumor Microenvironment Initiation and Maintaining

2021 ◽  
Author(s):  
Olga Kutova ◽  
Anton Pospelov ◽  
Irina Balalaeva
Keyword(s):  
Tumor Microenvironment ◽  
Purinergic Signaling ◽  
Large Body ◽  
Molecular Transport ◽  
Tumor Tissues ◽  
Cancer Initiation ◽  
Different Types ◽  
Channel Assembly ◽  
Extracellular Environment

The modern paradigm of studying the processes of carcinogenesis and vital activity of tumor tissues implies increased attention to constituents of tumor microenvironment (TME) and their interactions. These interactions between the cells in TME can be mediated via protein junctions of different types. Connexins (Cnxs) are one of the major contributors to intercellular communication. They form gap junctions responsible for the transfer of ions, metabolites, peptides, miRNA, etc. between neighboring tumor cells as well as between tumor and stromal cells. Cnx hemichannels mediate purinergic signaling and bidirectional molecular transport with the extracellular environment. Additionally, Cnxs were reported to localize in tumor-derived exosomes and facilitate the release of their cargo. A large body of evidence implies that the role of connexins in cancer is multifaceted. Pro- or anti-tumorigenic properties of connexins are determined by their abundance, localization and functionality as well as channel assembly and non-channel functions. In this review we have summarized the data on the Cnxs contribution in TME and to the cancer initiation and progression.

scholarly journals Prognostic Value of Genes Related with Tumor Microenvironment in Ovarian Cancer

2020 ◽  
Author(s):  
Shimei Li ◽  
Jiyi Yao ◽  
Shen Zhang ◽  
Xinchuan Zhou ◽  
Xinbao Zhao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Tumor Microenvironment ◽  
Poor Prognosis ◽  
External Validation ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Gene Expression Signatures ◽  
Poor Outcomes ◽  
Geo Database

Abstract Background Ovarian cancer (OV) is the fifth leading cause of cancer death among females. Growing evidence supports a key role of tumor microenvironment in growth, progress, and metastasis of OV. However, the impacts of gene expression signatures related with OV microenvironment on prognosis have not been well-established . This study aimed to apply ESTIMATE algorithm to extract genes related with tumor microenvironment that predicted poor outcomes in OV patients. Methods The gene expression profile of OV samples were downloaded from The Cancer Genome Atlas (TCGA) database. The immune scores and stromal scores of 469 OV samples were available based on the ESTIMATE algorithm. To better understand impacts of gene expression signatures related with OV microenvironment on prognosis, these samples were categorized based on their ESTIMATE scores into high and low score groups. A different OV cohort from the Gene Expression Omnibus (GEO) database was used for external validation. Results The molecular subtypes in OV patients were correlated with stromal scores, in which the mesenchymal subtype had the highest stromal scores (p < 0.0001). Poor prognosis were found in patients (especially for patients with overall survivals (OS) < 5 years) with higher stromal score (p = 0.0376). 449 differentially expressed genes (DEGs) in stromal scores group were identified and 26 DEGs were significantly associated with poor prognosis in OV patients (p < 0.05). Eventually, 6 genes have further validated to be significantly associated with poor outcomes in 40 patients from a different OV cohort of GEO database (p < 0.05). Conclusion In this study, several genes related with tumor microenvironment that predicted poor prognosis in OV patients were extracted. In addition, some previously overlooked genes could be potential prognostic biomarkers for OV.

scholarly journals Novel immune-related genes in the tumor microenvironment with prognostic value in breast cancer

2020 ◽  
Author(s):  
Wen Tan ◽  
Maomao Liu ◽  
Liangshan Wang ◽  
Yang Guo ◽  
Changsheng Wei ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Overall Survival ◽  
Tumor Microenvironment ◽  
T Cell Activation ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Network Analyses ◽  
Immune Score ◽  
Go Terms

Abstract Background: Breast cancer is one of the most frequently diagnosed cancers among women worldwide. Alterations in the tumor microenvironment (TME) have been increasingly recognized as key in the development and progression of breast cancer in recent years. To deeply comprehend the gene expression profiling of the TME and identify immunological targets, as well as determine the relationship between gene expression and different prognoses is highly critical. Results: The stromal/immune scores of breast cancer patients from The Cancer Genome Atlas were employed to comprehensively evaluate the TME. Although the TME did not correlate with the stages of breast cancer, it was closely associated with the subtypes of breast cancer and gene mutations (CDH1, TP53 and PTEN), and had immunological characteristics. Based on Gene Ontology (GO) functional enrichment analysis, the upregulated genes from the high vs low immune score groups were mainly involved in T cell activation, the external side of the plasma membrane, and receptor ligand activity. We further analyzed and screened the overlapping genes of the top 3 GO terms and upregulated differentially expressed genes (DEGs). Overall survival, time-dependent receiver operating characteristic (ROC), and protein-protein interaction (PPI) network analyses revealed that the genes of the top GO terms of the upregulated DEGs from the high vs low immune score groups exhibited better prognosis in breast cancer; 15 of them were related to good prognosis in breast cancer, especially CD226 and KLRC4-KLRK1.Conclusions: High CD226 and KLRC4-KLRK1 expression levels were identified and validated to correlate with better overall survival in specific stages or subtypes of breast cancer. CD226, KLRC4-KLRK1 and other new targets seem to be promising avenues for promoting antitumor targeted immunotherapy in breast cancer.

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