scholarly journals Integrated analysis of gene correlation reveals disordered relationship between metabolism and immunity in tumor microenvironment

2020 ◽  
Author(s):  
Zixi Chen ◽  
Jinfen Wei ◽  
Yuchen Yuan ◽  
Ying Cui ◽  
Yanyu Zhang ◽  
...  
Keyword(s):  
Inositol Phosphate ◽  
Functional Enrichment ◽  
Integrated Analysis ◽  
Cancer Type ◽  
Normal Tissues ◽  
Gene Pairs ◽  
Complex Interactions ◽  
Tumor Tissues ◽  
Cancer Types ◽  
Immune Related Genes

AbstractBackgroundMetabolism reprogramming and immune evasion are the most fundamental hallmarks for cancer survival. The complex interactions between metabolism and immune systems in tumors and their microenvironment is complicated. Researching on the correlation changes between metabolic and immune related-genes in normal and tumor tissues would help to reveal these complex interactions.MethodsIn this study, the mRNA profiles across 11 cancer types was obtained from The Cancer Genome Atlas (TCGA). Then, the spearman’s correlation coefficient was calculated between metabolic and immune related-genes for each sample group.ResultsOur results showed that the number of correlated gene pairs was reduced significantly in tumor tissues compared with those of normal tissue, especially in KIRC, KIRP and STAD. Functional enrichment analysis for the universal (the pairs appeared in more than 2 cancer types) and specific (the pairs only in one specific cancer type) gene pairs across cancer types revealed top pathways which appeared in tumor and normal samples, such as phosphatidylinositol signaling system and inositol phosphate metabolism. Thereinto, the pairs in normal tissues missing in tumors may indicate they are important factors affecting immune system, such as, DGKs and PIP4ks. The correlation analysis between immune checkpoint and metabolism genes also showed a reduced correlation in tumor and had the tissue specificity, such as, FUT8 was strongly correlated with PDCD1 in the HC of STAD and they had a weaker correlation in other normal tissues and tumor types.ConclusionsOur study provides a novel strategy for investigating interaction of tumor immune and metabolism in microenvironment and offers some key points for exploring new targets including metabolic targets and immunomodulator of immune checkpoints.

scholarly journals Integrated Analysis of MicroRNA (miRNA) and mRNA Profiles Reveals Reduced Correlation between MicroRNA and Target Gene in Cancer

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Xingsong Li ◽  
Xiaokang Yu ◽  
Yuting He ◽  
Yuhuan Meng ◽  
Jinsheng Liang ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Mirna Target ◽  
Target Gene ◽  
Target Genes ◽  
Integrated Analysis ◽  
Cancer Type ◽  
Gene Pairs ◽  
Cancer Types

Background. Accumulating evidences demonstrated that microRNA-target gene pairs were closely related to tumorigenesis and development. However, the correlation between miRNA and target gene was insufficiently understood, especially its changes between tumor and normal tissues. Objectives. The aim of this study was to evaluate the changes of correlation of miRNAs-target pairs between normal and tumor. Materials and Methods. 5680 mRNA and 5740 miRNA expression profiles of 11 major human cancers were downloaded from the Cancer Genome Atlas (TCGA). The 11 cancer types were bladder urothelial carcinoma, breast invasive carcinoma, head and neck squamous cell carcinoma, kidney chromophobe, kidney renal clear cell carcinoma, kidney renal papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, stomach adenocarcinoma, and thyroid carcinoma. For each cancer type, we firstly obtained differentially expressed miRNAs (DEMs) and genes (DEGs) in tumor and then acquired critical miRNA-target gene pairs by combining DEMs, DEGs and two experimentally validated miRNA-target interaction databases, miRTarBase and miRecords. We collected samples with both miRNA and mRNA expression values and performed a correlation analysis by Pearson method for miRNA-target pairs in normal and tumor, respectively. Results. We totally got 4743 critical miRNA-target pairs across 11 cancer types, and 4572 of them showed weaker correlation in tumor than in normal. The average correlation coefficients of miRNA-target pairs were different greatly between normal (-0.38 ~ -0.61) and tumor (-0.04 ~ -0.26) for 11 cancer type. The pan-cancer network, which consisted of 108 edges connecting 35 miRNAs and 89 target genes, showed the interactions of pairs appeared in multicancers. Conclusions. This comprehensive analysis revealed that correlation between miRNAs and target genes was greatly reduced in tumor and these critical pairs we got were involved in cellular adhesion, proliferation, and migration. Our research could provide opportunities for investigating cancer molecular regulatory mechanism and seeking therapeutic targets.

scholarly journals Analyzing cancer gene expression data through the lens of normal tissue-specificity

2021 ◽  
Author(s):  
H. Robert Frost
Keyword(s):  
Gene Expression ◽  
Normal Tissue ◽  
Tissue Specificity ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Genetic Alterations ◽  
Cancer Type ◽  
Tissue Specific ◽  
Normal Tissues ◽  
Cancer Types

AbstractThe genetic alterations that underlie cancer development are highly tissue-specific with the majority of driving alterations occurring in only a few cancer types and with alterations common to multiple cancer types often showing a tissue-specific functional impact. This tissue-specificity means that the biology of normal tissues carries important information regarding the pathophysiology of the associated cancers, information that can be leveraged to improve the power and accuracy of cancer genomic analyses. Research exploring the use of normal tissue data for the analysis of cancer genomics has primarily focused on the paired analysis of tumor and adjacent normal samples. Efforts to leverage the general characteristics of normal tissue for cancer analysis has received less attention with most investigations focusing on understanding the tissue-specific factors that lead to individual genomic alterations or dysregulated pathways within a single cancer type. To address this gap and support scenarios where adjacent normal tissue samples are not available, we explored the genome-wide association between the transcriptomes of 21 solid human cancers and their associated normal tissues as profiled in healthy individuals. While the average gene expression profiles of normal and cancerous tissue may appear distinct, with normal tissues more similar to other normal tissues than to the associated cancer types, when transformed into relative expression values, i.e., the ratio of expression in one tissue or cancer relative to the mean in other tissues or cancers, the close association between gene activity in normal tissues and related cancers is revealed. As we demonstrate through an analysis of tumor data from The Cancer Genome Atlas and normal tissue data from the Human Protein Atlas, this association between tissue-specific and cancer-specific expression values can be leveraged to improve the prognostic modeling of cancer, the comparative analysis of different cancer types, and the analysis of cancer and normal tissue pairs.

scholarly journals Putatively cancer-specific exon–exon junctions are shared across patients and present in developmental and other non-cancer cells

NAR Cancer ◽  
2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Julianne K David ◽  
Sean K Maden ◽  
Benjamin R Weeder ◽  
Reid F Thompson ◽  
Abhinav Nellore
Keyword(s):  
Rna Splicing ◽  
Tissue Expression ◽  
The Body ◽  
The Cancer Genome Atlas ◽  
Cancer Type ◽  
Rna Seq ◽  
Normal Tissues ◽  
Cancer Types ◽  
Exon Junctions ◽  
Cancer Tissues

Abstract This study probes the distribution of putatively cancer-specific junctions across a broad set of publicly available non-cancer human RNA sequencing (RNA-seq) datasets. We compared cancer and non-cancer RNA-seq data from The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression (GTEx) Project and the Sequence Read Archive. We found that (i) averaging across cancer types, 80.6% of exon–exon junctions thought to be cancer-specific based on comparison with tissue-matched samples (σ = 13.0%) are in fact present in other adult non-cancer tissues throughout the body; (ii) 30.8% of junctions not present in any GTEx or TCGA normal tissues are shared by multiple samples within at least one cancer type cohort, and 87.4% of these distinguish between different cancer types; and (iii) many of these junctions not found in GTEx or TCGA normal tissues (15.4% on average, σ = 2.4%) are also found in embryological and other developmentally associated cells. These findings refine the meaning of RNA splicing event novelty, particularly with respect to the human neoepitope repertoire. Ultimately, cancer-specific exon–exon junctions may have a substantial causal relationship with the biology of disease.

scholarly journals Multi-omics characterization and validation of invasiveness-related molecular features across multiple cancer types

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Guoshu Bi ◽  
Jiaqi Liang ◽  
Yuansheng Zheng ◽  
Runmei Li ◽  
Mengnan Zhao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Predictive Biomarker ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Integrated Analysis ◽  
Multiple Cancer ◽  
Tumor Invasiveness ◽  
Molecular Features ◽  
Cancer Types

Abstract Background Tumor invasiveness reflects many biological changes associated with tumorigenesis, progression, metastasis, and drug resistance. Therefore, we performed a systematic assessment of invasiveness-related molecular features across multiple human cancers. Materials and methods Multi-omics data, including gene expression, miRNA, DNA methylation, and somatic mutation, in approximately 10,000 patients across 30 cancer types from The Cancer Genome Atlas, Gene Expression Omnibus, PRECOG, and our institution were enrolled in this study. Results Based on a robust gene signature, we established an invasiveness score and found that the score was significantly associated with worse prognosis in almost all cancers. Then, we identified common invasiveness-associated dysregulated molecular features between high- and low-invasiveness score group across multiple cancers, as well as investigated their mutual interfering relationships thus determining whether the dysregulation of invasiveness-related genes was caused by abnormal promoter methylation or miRNA expression. We also analyzed the correlations between the drug sensitivity data from cancer cell lines and the expression level of 685 invasiveness-related genes differentially expressed in at least ten cancer types. An integrated analysis of the correlations among invasiveness-related genetic features and drug response were conducted in esophageal carcinoma patients to outline the complicated regulatory mechanism of tumor invasiveness status in multiple dimensions. Moreover, functional enrichment suggests the invasiveness score might serve as a predictive biomarker for cancer patients receiving immunotherapy. Conclusion Our pan-cancer study provides a comprehensive atlas of tumor invasiveness and may guide more precise therapeutic strategies for tumor patients.

scholarly journals Transcription Elongation Factor A (SII) -Like (TCEAL) Gene Family Member-TCEAL2: A Novel Prognostic Marker in Pan-Cancer

2022 ◽  
Author(s):  
Yu Sun ◽  
Jun Zhao
Keyword(s):  
Prognostic Marker ◽  
Elongation Factor ◽  
Tumor Infiltration ◽  
Normal Tissues ◽  
Gene Enrichment ◽  
Tumor Tissues ◽  
Tubulin Binding ◽  
Immune Related Genes ◽  
Pan Cancer ◽  
Geo Database

Abstract Background: Cancer is the leading cause of death in the world. The mechanism is not fully elucidated and the therapeutic effect is also unsatisfactory. In our study, we aim to find new target gene in pan-cancer.Methods: Differentially expressed genes (DEGs) was screened out in various types of cancers from GEO database. The expression of DEG (TCEAL2) in tumor cell lines, normal tissues and tumor tissues was calculated. Then the clinical characteristics, DNA methylation, tumor infiltration and gene enrichment of TCEAL2 was studied. Results: TCEAL2 expressions were down-regulated in most cancers. Its expression and methylation were positively or negatively associated with prognosis in different cancers. The tumor infiltration results revealed that TCEAL2 was significantly related with many immune cells especially NK cells and immune-related genes in majority cancers. Furthermore, tau protein and tubulin binding were involved in the molecular function mechanisms of TCEAL2. Conclusion: TCEAL2 may be a novel prognostic marker in different cancers and may affect tumor through immune infiltration.

scholarly journals COL5A3 is a prognostic biomarker and correlated with immune infiltrates in pancreatic cancer

2021 ◽  
Author(s):  
Yongjie Li ◽  
Min Zeng ◽  
Ting Wang ◽  
Feng Jiang
Keyword(s):  
Pancreatic Cancer ◽  
Roc Curve ◽  
Prognostic Biomarker ◽  
Expression Profiles ◽  
Functional Enrichment ◽  
High Expression ◽  
Immune Infiltration ◽  
Normal Tissues ◽  
Kaplan Meier ◽  
Tumor Tissues

Abstract Background Pancreatic cancer is a malignant tumor of digestive system with high fatality rate, and its prognosis is very poor. Type Ⅴ collagen α3 (COL5A3) is highly expressed in a variety of tumor tissues, but its prognostic value and immune infiltration in pancreatic cancer are still unclear. Therefore, we evaluated the prognostic role of COL5A3 in pancreatic cancer and its correlation with immune infiltration. Methods The transcriptional expression profiles of COL5A3 in pancreatic cancer and normal tissues were downloaded from the Cancer Genome Atlas (TCGA). In the GEO (Gene expression omnibus) dataset (GSE16515), we compared the expression of COL5A3 in normal and tumor tissues. The expression of COL5A3 protein was evaluated by the human protein atlas (THPA). The effect of COL5A3 on survival was evaluated by Kaplan-Meier method. Receiver operating characteristic (ROC) curve was used to distinguish pancreatic cancer from paracancerous normal tissues. Protein-protein interaction (PPI) network was constructed by the STRING. Use the "ClusterProfiler" package for functional enrichment analysis. Tumor immune estimation resource (TIMER) and tumor-immune system interaction database (TISIDB) were used to determine the relationship between COL5A3mRNA expression and immune infiltration. Results Compared with normal tissues, COL5A3 is highly expressed in pancreatic cancer tissues. The high expression of COL5A3 is related to the poor clinicopathological features and poor prognosis of pancreatic cancer. Kaplan-Meier survival analysis showed that the prognosis of pancreatic cancer patients with high expression of COL5A3 was worse than that of patients with low expression of COL5A3. ROC curve shows that COL5A3 has high sensitivity and specificity in the diagnosis of pancreatic cancer. Correlation analysis showed that the expression of COL5A3mRNA was related to immune cell infiltration. Conclusion This study reveals for the first time that COL5A3 may be a new prognostic biomarker related to immune infiltration and provide a new target for the diagnosis and treatment of pancreatic cancer.

scholarly journals Integrated Genomic Analysis of Mitochondrial RNA Processing in Human Cancers

2016 ◽  
Author(s):  
Youssef Idaghdour ◽  
Alan Hodgkinson
Keyword(s):  
Mitochondrial Function ◽  
Rna Processing ◽  
Genomic Analysis ◽  
Therapeutic Interventions ◽  
Mitochondrial Rna ◽  
Normal Tissues ◽  
Human Cancers ◽  
Tumor Tissues ◽  
Cancer Types ◽  
Post Transcriptional Regulation

AbstractAlterations to mitochondrial function and mutations in mitochondrial genes have been reported for a wide variety of cancers, however the mitochondrial transcriptome remains largely unexplored in cancer despite an emerging appreciation of the role that post-transcriptional regulation plays in the etiology of these diseases. Here, we quantify and assess changes to mitochondrial RNA processing in human cancers using integrated genomic analysis of RNA Sequencing and genotyping data from 1226 samples across 12 different cancer types. We find significant changes to m1A and m1G post-transcriptional methylation rates at functionally important positions in mitochondrial tRNAs in tumor tissues across all cancers. Pathways of RNA processing are strongly associated with methylation rates in normal tissues (P=2.85×10-27), yet these associations are lost in tumors. Furthermore, we report 18 gene-by-disease-state interactions where altered methylation rates occur under cancer status conditional on genotype, implicating genes associated with mitochondrial function or cancer (e.g. CACNA2D2, LMO2 and FLT3) and suggesting that nuclear genetic variation can potentially modulate an individual’s ability to maintain unaltered rates of mitochondrial RNA processing under cancer status. Finally, we report a significant association between the magnitude of methylation rate changes in tumors and patient survival outcomes. These results highlight mitochondrial post-transcriptional events as a clinically relevant mechanism and as a theme for the further investigation of cancer processes, biomarkers and therapeutic interventions.

scholarly journals Mining hub genes correlated with immune infiltrating level across multiple tumors microenvironment

2020 ◽  
Author(s):  
Ning Zhao ◽  
Liang Wu ◽  
Zili Zhou ◽  
Xudan Zhang ◽  
Shengbo Han ◽  
...  
Keyword(s):  
Immune Response ◽  
Immune Cells ◽  
Tyrosine Phosphatase ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Cancer Type ◽  
Hub Genes ◽  
Survival Prognosis ◽  
Multiple Tumors ◽  
Cancer Types

Abstract Background: Previous studies revealed that cancer-associated differentially expressed genes (DEGs) in an independent cancer type are rarely related to the tumorigenesis and metastasis, while the common DEGs across multiple types of cancer may be proved as potential oncogenes or tumor suppressors. Although tumor-infiltrating immune cells have been reported to be associated with prognosis in multiple types of cancer, the hub genes regulating immune cells function in different cancer types remain unclear. Methods: To screen for the hub genes regulating immune infiltrating level across multiple tumors microenvironment, the raw data containing RNA sequencing and clinical information from TCGA database and immune scores from ESTIMATE website across 25 cancer types were obtained. Results: Based on the immune scores, all cases were categorized into high-score and low-score groups. Kaplan–Meier survival analysis demonstrated that a strong correlation between immune infiltrating level and survival prognosis was found in six cancer types. The functional enrichment analysis of common DEGs revealed that infection and immune response are the most prominent biological characteristics. Subsequently, the twelve common DEGs with prognostic value were identified as candidate hub genes and were adopted to construct the PPI network. Because of highly interconnected with other hub genes, protein tyrosine phosphatase non-receptor type 6 (PTPN6) was selected as the real hub gene across the six immune-specific tumors. Finally, a significant correlation between PTPN6 and immune infiltrating level, and immune marker sets of various immune cells were observed. Conclusion: PTPN6 may play a vital role in regulating immune response for tumor development, due to its significant correlation with tumor-infiltrating immune cells in multiple cancers.

scholarly journals Analyzing cancer gene expression data through the lens of normal tissue-specificity

2021 ◽  
Vol 17 (6) ◽  
pp. e1009085
Author(s):  
H. Robert Frost
Keyword(s):  
Gene Expression ◽  
Normal Tissue ◽  
Tissue Specificity ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Genetic Alterations ◽  
Cancer Type ◽  
Tissue Specific ◽  
Normal Tissues ◽  
Cancer Types

The genetic alterations that underlie cancer development are highly tissue-specific with the majority of driving alterations occurring in only a few cancer types and with alterations common to multiple cancer types often showing a tissue-specific functional impact. This tissue-specificity means that the biology of normal tissues carries important information regarding the pathophysiology of the associated cancers, information that can be leveraged to improve the power and accuracy of cancer genomic analyses. Research exploring the use of normal tissue data for the analysis of cancer genomics has primarily focused on the paired analysis of tumor and adjacent normal samples. Efforts to leverage the general characteristics of normal tissue for cancer analysis has received less attention with most investigations focusing on understanding the tissue-specific factors that lead to individual genomic alterations or dysregulated pathways within a single cancer type. To address this gap and support scenarios where adjacent normal tissue samples are not available, we explored the genome-wide association between the transcriptomes of 21 solid human cancers and their associated normal tissues as profiled in healthy individuals. While the average gene expression profiles of normal and cancerous tissue may appear distinct, with normal tissues more similar to other normal tissues than to the associated cancer types, when transformed into relative expression values, i.e., the ratio of expression in one tissue or cancer relative to the mean in other tissues or cancers, the close association between gene activity in normal tissues and related cancers is revealed. As we demonstrate through an analysis of tumor data from The Cancer Genome Atlas and normal tissue data from the Human Protein Atlas, this association between tissue-specific and cancer-specific expression values can be leveraged to improve the prognostic modeling of cancer, the comparative analysis of different cancer types, and the analysis of cancer and normal tissue pairs.

scholarly journals An integrated transcriptomics and proteomics study of Head and Neck Squamous Cell Carcinoma – methodological and analytical considerations.

2015 ◽  
Author(s):  
Anupama Rajan Bhat ◽  
Manoj Kumar Gupta ◽  
Priya Krithivasan ◽  
Kunal Dhas ◽  
Jayalakshmi Nair ◽  
...  
Keyword(s):  
Data Analysis ◽  
Proteome Analysis ◽  
Peptide Identification ◽  
Differentially Expressed ◽  
Integrated Analysis ◽  
Primary Tumors ◽  
Normal Tissues ◽  
Tumor Tissues ◽  
The Individual ◽  
Integrated Data Analysis

High throughput molecular profiling and integrated data analysis with tumor tissues require overcoming challenges like tumor heterogeneity and tissue paucity. This study is an attempt to understand and optimize various steps during tissue processing and in establishing pipelines essential for integrated analysis. Towards this effort, we subjected laryngo-pharyngeal primary tumors and the corresponding adjacent normal tissues (n=2) to two RNA and protein isolation methods, one wherein RNA and protein were isolated from the same tissue sequentially (Method 1) and second, wherein the extraction was carried out using two independent methods (Method 2). RNA and protein from both methods were subjected to RNA-seq and iTRAQ based LC-MS/MS analysis. Transcript and peptide identification and quantification was followed by both individual -ome and integrated data analysis. As a result of this analysis, we identified a higher number of total, as well as differentially expressed (DE) transcripts (1329 vs 1134) and proteins (799 vs 408) with fold change ≥ 2.0, in Method 1. Among these, 173 and 86 entities were identified by both transcriptome and proteome analysis in Method 1 and 2, respectively, with higher concordance in the regulation trends observed in the former. The significant cancer related pathways enriched with the individual DE transcript or protein data were similar in both the methods. However, the entities mapping to them were different, allowing enhanced view of the pathways identified after integration of the data and subsequent mapping. The concordant DE transcripts and proteins also revealed key molecules of the pathways with important roles in cancer development. This study thus demonstrates that sequential extraction of the RNA and proteins from the same tissue allows for better profiling of differentially expressed entities and a more accurate integrated data analysis.

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