Multi-omics characterization and validation of invasiveness-related molecular features across multiple 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.

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Integrated Analysis Reveals hsa-miR-142 as a Representative of a Lymphocyte-Specific Gene Expression and Methylation Signature

Cancer Informatics ◽

10.4137/cin.s9037 ◽

2012 ◽

Vol 11 ◽

pp. CIN.S9037 ◽

Cited By ~ 14

Author(s):

Bill Andreopoulos ◽

Dimitris Anastassiou

Keyword(s):

Gene Expression ◽

Cell Adhesion ◽

The Cancer Genome Atlas ◽

Integrated Analysis ◽

Specific Gene ◽

Data Types ◽

Multiple Cancer ◽

Specific Expression ◽

Cancer Types ◽

Cell Cell

Gene expression profiling has provided insights into different cancer types and revealed tissue-specific expression signatures. Alterations in microRNA expression contribute to the pathogenesis of many types of human diseases. Few studies have integrated all levels of gene expression, miRNA and methylation to uncover correlations between these data types. We performed an integrated profiling to discover instances of miRNAs associated with a gene expression and DNA methylation signature across multiple cancer types. Using data from The Cancer Genome Atlas (TCGA), we revealed a concordant gene expression and methylation signature associated with the microRNA hsa-miR-142 across the same samples. In all cancer types examined, we found a signature of co-expression of a gene set R and methylated sites M, which correlate positively (M+) or negatively (M–) with the expression of hsa-miR-142. The set R consistently contains many genes, such as TRAF3IP3, NCKAP1L, CD53, LAPTM5, PTPRC, EVI2B, DOCK2, LCP2, CYBB and FYB. The signature is preserved across glioblastoma, ovarian, breast, colon, kidney, lung, uterine and rectum cancer. There is 28% overlap of methylation sites in M between glioblastoma (GBM) and ovarian cancer. There is 60% overlap of genes in R between GBM and ovarian ( P = 1.3e−-11). Most of the genes in R are known to be expressed in lymphocytes and haematopoietic stem cells, while M reflects membrane proteins involved in cell-cell adhesion functions. We speculate that the hsa-miR-142 associated signature may signal haematopoietic-specific processes and an accumulation of methylation events triggering a progressive loss of cell-cell adhesion. We also observed that GBM samples belonging to the proneural subtype tend to have underexpressed hsa-miR-142 and R genes, hypomethylated M+ and hypermethylated M–, while the mesenchymal samples have the opposite profile.

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BART Cancer: a web resource for transcriptional regulators in cancer genomes

NAR Cancer ◽

10.1093/narcan/zcab011 ◽

2021 ◽

Vol 3 (1) ◽

Author(s):

Zachary V Thomas ◽

Zhenjia Wang ◽

Chongzhi Zang

Keyword(s):

Gene Expression ◽

Cancer Research ◽

Computational Prediction ◽

Transcriptional Regulators ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Regulation Of Transcription ◽

Data Resource ◽

Web Resource ◽

Cancer Types

Abstract Dysregulation of gene expression plays an important role in cancer development. Identifying transcriptional regulators, including transcription factors and chromatin regulators, that drive the oncogenic gene expression program is a critical task in cancer research. Genomic profiles of active transcriptional regulators from primary cancer samples are limited in the public domain. Here we present BART Cancer (bartcancer.org), an interactive web resource database to display the putative transcriptional regulators that are responsible for differentially regulated genes in 15 different cancer types in The Cancer Genome Atlas (TCGA). BART Cancer integrates over 10000 gene expression profiling RNA-seq datasets from TCGA with over 7000 ChIP-seq datasets from the Cistrome Data Browser database and the Gene Expression Omnibus (GEO). BART Cancer uses Binding Analysis for Regulation of Transcription (BART) for predicting the transcriptional regulators from the differentially expressed genes in cancer samples compared to normal samples. BART Cancer also displays the activities of over 900 transcriptional regulators across cancer types, by integrating computational prediction results from BART and the Cistrome Cancer database. Focusing on transcriptional regulator activities in human cancers, BART Cancer can provide unique insights into epigenetics and transcriptional regulation in cancer, and is a useful data resource for genomics and cancer research communities.

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TISCH: a comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment

Nucleic Acids Research ◽

10.1093/nar/gkaa1020 ◽

2020 ◽

Vol 49 (D1) ◽

pp. D1420-D1430

Author(s):

Dongqing Sun ◽

Jin Wang ◽

Ya Han ◽

Xin Dong ◽

Jun Ge ◽

...

Keyword(s):

Gene Expression ◽

Tumor Microenvironment ◽

Single Cell ◽

Large Scale ◽

Differential Expression Analysis ◽

Enrichment Analysis ◽

Functional Enrichment ◽

Multiple Cancer ◽

Web Resource ◽

Cancer Types

Abstract Cancer immunotherapy targeting co-inhibitory pathways by checkpoint blockade shows remarkable efficacy in a variety of cancer types. However, only a minority of patients respond to treatment due to the stochastic heterogeneity of tumor microenvironment (TME). Recent advances in single-cell RNA-seq technologies enabled comprehensive characterization of the immune system heterogeneity in tumors but posed computational challenges on integrating and utilizing the massive published datasets to inform immunotherapy. Here, we present Tumor Immune Single Cell Hub (TISCH, http://tisch.comp-genomics.org), a large-scale curated database that integrates single-cell transcriptomic profiles of nearly 2 million cells from 76 high-quality tumor datasets across 27 cancer types. All the data were uniformly processed with a standardized workflow, including quality control, batch effect removal, clustering, cell-type annotation, malignant cell classification, differential expression analysis and functional enrichment analysis. TISCH provides interactive gene expression visualization across multiple datasets at the single-cell level or cluster level, allowing systematic comparison between different cell-types, patients, tissue origins, treatment and response groups, and even different cancer-types. In summary, TISCH provides a user-friendly interface for systematically visualizing, searching and downloading gene expression atlas in the TME from multiple cancer types, enabling fast, flexible and comprehensive exploration of the TME.

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Identifying and characterizing lincRNA genomic clusters reveals its cooperative functions in human cancer

Journal of Translational Medicine ◽

10.1186/s12967-021-03179-5 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Hanxiao Zhou ◽

Yue Gao ◽

Xin Li ◽

Shipeng Shang ◽

Peng Wang ◽

...

Keyword(s):

Human Cancer ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Computational Method ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Pathophysiological Mechanism ◽

Multiple Cancer ◽

Cancer Types ◽

Genomic Clusters

Abstract Background Emerging evidence has revealed that some long intergenic non-coding RNAs (lincRNAs) are likely to form clusters on the same chromosome, and lincRNA genomic clusters might play critical roles in the pathophysiological mechanism. However, the comprehensive investigation of lincRNA clustering is rarely studied, particularly the characterization of their functional significance across different cancer types. Methods In this study, we firstly constructed a computational method basing a sliding window approach for systematically identifying lincRNA genomic clusters. We then dissected these lincRNA genomic clusters to identify common characteristics in cooperative expression, conservation among divergent species, targeted miRNAs, and CNV frequency. Next, we performed comprehensive analyses in differentially-expressed patterns and overall survival outcomes for patients from The Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression (GTEx) across multiple cancer types. Finally, we explored the underlying mechanisms of lincRNA genomic clusters by functional enrichment analysis, pathway analysis, and drug-target interaction. Results We identified lincRNA genomic clusters according to the algorithm. Clustering lincRNAs tended to be co-expressed, highly conserved, targeted by more miRNAs, and with similar deletion and duplication frequency, suggesting that lincRNA genomic clusters may exert their effects by acting in combination. We further systematically explored conserved and cancer-specific lincRNA genomic clusters, indicating they were involved in some important mechanisms of disease occurrence through diverse approaches. Furthermore, lincRNA genomic clusters can serve as biomarkers with potential clinical significance and involve in specific pathological processes in the development of cancer. Moreover, a lincRNA genomic cluster named Cluster127 in DLK1-DIO3 imprinted locus was discovered, which contained MEG3, MEG8, MEG9, MIR381HG, LINC02285, AL132709.5, and AL132709.1. Further analysis indicated that Cluster127 may have the potential for predicting prognosis in cancer and could play their roles by participating in the regulation of PI3K-AKT signaling pathway. Conclusions Clarification of the lincRNA genomic clusters specific roles in human cancers could be beneficial for understanding the molecular pathogenesis of different cancer types.

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Higher prevalence of homologous recombination-deficiency in lung squamous carcinoma from African Americans

10.1101/651794 ◽

2019 ◽

Author(s):

Sanju Sinha ◽

Khadijah A. Mitchell ◽

Adriana Zingone ◽

Elise Bowman ◽

Neelam Sinha ◽

...

Keyword(s):

Homologous Recombination ◽

Copy Number ◽

Squamous Carcinoma ◽

The Cancer Genome Atlas ◽

Multiple Cancer ◽

Homologous Recombination Deficiency ◽

Molecular Features ◽

Incidence And Mortality ◽

Cancer Types ◽

Lung Squamous Carcinoma

AbstractTo improve our understanding of the longstanding disparities in incidence and mortality across multiple cancer types among minority populations, we performed a systematic comparative analysis of molecular features in tumors from African American (AA) and European American (EA) ancestry. Our pan-cancer analysis on the cancer genome atlas (TCGA) and a more focused analysis of genome-wide somatic copy number profiles integrated with tumor-normal RNA sequencing in a racially balanced cohort of 222 non-small cell lung cancers (NSCLC) reveals more aggressive genomic characteristics of AA tumors. In general, we find AA tumors exhibit higher genomic instability (GI), homologous recombination-deficiency (HRD) levels, and more aggressive molecular features such as chromothripsis across many cancer types, including lung squamous carcinoma (LUSC). GI and HRD levels are strongly correlated across AA tumors, indicating that HRD plays an important role in GI in these patients. The prevalence of germline HRD is higher in AA tumors, suggesting that the somatic differences observed have genetic ancestry origins. Finally, we identify AA-specific copy number-based arm, focal and gene level recurrent features in lung cancer, including a higher frequency of PTEN deletion and KRAS amplification and a lower frequency of CDKN2A deletion. These results highlight the importance of including minority and under-represented populations in genomics research and may have therapeutic implications.

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Identification of Novel Potential Biomarkers in Hepatocarcinoma Cancer; A Transcriptome Analysis

10.21203/rs.3.rs-154350/v1 ◽

2021 ◽

Author(s):

Mohib kakar ◽

Muhammad Mehboob ◽

Muhammad Akram ◽

Imran Iqbal ◽

Hafza Ijaz ◽

...

Keyword(s):

Gene Expression ◽

Cancer Progression ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Predictive Biomarker ◽

Gene Expression Omnibus ◽

Functional Enrichment ◽

Molecular Therapy ◽

Hub Genes ◽

Healthy Control

Abstract The goal of this study was to understand possible core genes associated with hepatocellular carcinoma (HCC) pathogenesis and prognosis. Gene Expression Omnibus (GEO) contains datasets of gene expression, miRNA and methylation patterns of diseased and healthy/control patients. GSE62232 Dataset was selected by employing the server GEO. A total of 91 samples were collected, including 81 HCC samples and 10 healthy samples as control. GSE62232 was analyzed through GEO2R, and functional enrichment analysis was performed to extract rational information from a set of DEGs. The protein-protein relationship networking search method was used for extracting interacting genes. MCC method was used to calculate the top 10 genes according to their importance. Hub genes in the network were analyzed using GEPIA to estimate the effect of their differential expression on cancer progression. We identified the top 10 hub genes through Cytohubba plugin. These genes include cell cycle regulatory cyclins and cyclin-dependent proteins CCNA2, CCNB1 and CDK1. The pathogenesis and prognosis of HCC may be directly linked with the aforementioned genes. In this analysis, we found critical genes for HCC that showed recommendations for more diagnostic and predictive biomarker studies that could promote selective molecular therapy for HCC.

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Pan-cancer driver copy number alterations identified by joint expression/CNA data analysis

Scientific Reports ◽

10.1038/s41598-020-74276-6 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Gaojianyong Wang ◽

Dimitris Anastassiou

Keyword(s):

Gene Expression ◽

Copy Number ◽

The Cancer Genome Atlas ◽

Copy Number Alterations ◽

Multiple Cancer ◽

Cancer Driver ◽

Large Gene ◽

Cancer Genome Atlas ◽

Cancer Types ◽

Pan Cancer

Abstract Analysis of large gene expression datasets from biopsies of cancer patients can identify co-expression signatures representing particular biomolecular events in cancer. Some of these signatures involve genomically co-localized genes resulting from the presence of copy number alterations (CNAs), for which analysis of the expression of the underlying genes provides valuable information about their combined role as oncogenes or tumor suppressor genes. Here we focus on the discovery and interpretation of such signatures that are present in multiple cancer types due to driver amplifications and deletions in particular regions of the genome after doing a comprehensive analysis combining both gene expression and CNA data from The Cancer Genome Atlas.

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Identification of MEG8-miR378d-SOBP Axis as a Novel Regulatory Network and Associated With Immune Infiltrates in Ovarian Carcinoma by Integrated Bioinformatics Analysis

10.21203/rs.3.rs-76980/v1 ◽

2020 ◽

Author(s):

Jian Lei ◽

Zhen-Yu He ◽

Jun Wang ◽

Min Hu ◽

Ping Zhou ◽

...

Keyword(s):

Gene Expression ◽

Ovarian Cancer ◽

Bioinformatics Analysis ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Gene Expression Omnibus ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Human Protein Atlas

Abstract BackgroundTo investigate the potential molecular mechanism of ovarian cancer (OC) evolution and immunological correlation using the integrated bioinformatics analysis.MethodsData from the Gene Expression Omnibus (GEO) was used to gain differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis were completed by utilizing the Database for Annotation, Visualization, and Integrated Discovery (DAVID). After multiple validation via The Cancer Genome Atlas (TCGA), Gene Expression Profiling Interactive Analysis 2 (GEPIA 2), the Human Protein Atlas (HPA) and Kaplan-Meier (KM) plotter, immune logical relationships of the key gene SOBP were evaluated based on Tumor Immune Estimation Resource (TIMER), and Gene Set Enrichment Analysis (GSEA) software. Finally, the lncRNAs-miRNAs-mRNAs sub-network was predicted by starBase, Targetscan, miRBD, and LncBase, individually. Correlation of expression and prognosis for mRNAs, miRNAs and lncRNAs were confirmed by TCGA, GEPIA 2, starBase, and KM.ResultsA total of 192 shared DEGs were discovered from the four data sets, including 125 upregulated and 67 downregulated genes. Functional enrichment analysis presented that they were mainly enriched in cartilage development, pathway in PI3K-Akt signaling pathway. Lower expression of SOBP was the independent prognostic factor for inferior prognosis in OC patients. Intriguingly, downregulated SOBP enhanced the infiltration levels of B cells, CD8+ T cells, Macrophage, Neutrophil and Dendritic cells. GSEA also disclosed low SOBP showed significantly association with the activation of various immune-related pathways. Finally, we firstly reported that MEG8-miR378d-SOBP axis was linked to development and prognosis of ovarian cancer through regulating cytokines pathway.Conclusions Our study establishes a novel MEG8-miR378d-SOBP axis in the development and prognosis of OC, and the triple sub-network probably affects the progression of ovarian tumor by regulating cytokines pathway.

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An independent poor-prognosis subtype of hepatocellular carcinoma based on the tumor microenvironment

Journal of International Medical Research ◽

10.1177/0300060520980646 ◽

2021 ◽

Vol 49 (2) ◽

pp. 030006052098064

Author(s):

Junfeng Wang ◽

Jianying Lou ◽

Lei Fu ◽

Qu Jin

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Tumor Microenvironment ◽

Poor Prognosis ◽

Molecular Mechanisms ◽

Immune Cell ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Using Data

Background Hepatocellular carcinoma (HCC) is a highly malignant tumor with a particularly poor prognosis. The tumor microenvironment (TME) is closely associated with tumorigenesis, progression, and treatment. However, the relationship between TME genes and HCC patient prognosis is poorly understood. Methods In this study, we identified two prognostic subtypes based on the TME using data from The Cancer Genome Atlas and Gene Expression Omnibus. The Microenvironment Cell Populations-counter method was used to evaluate immune cell infiltration in HCC. Differentially expressed genes between molecular subtypes were calculated with the Limma package, and clusterProfiler was used for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analyses to identify genes related to the independent subtypes. We also integrated mRNA expression data into our bioinformatics analysis. Results We identified 4227 TME-associated genes and 640 genes related to the prognosis of HCC. We defined two major subtypes (Clusters 1 and 2) based on the analysis of TME-associated gene expression. Cluster 1 was characterized by increased expression of immune-associated genes and a worse prognosis than Cluster 2. Conclusions The identification of these HCC subtypes based on the TME provides further insight into the molecular mechanisms and prediction of HCC prognosis.

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BART Cancer: a web resource for transcriptional regulators in cancer genomes

10.1101/2020.12.17.423327 ◽

2020 ◽

Author(s):

Zachary V. Thomas ◽

Zhenjia Wang ◽

Chongzhi Zang

Keyword(s):

Gene Expression ◽

Cancer Research ◽

Computational Prediction ◽

Transcriptional Regulators ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Regulation Of Transcription ◽

Data Resource ◽

Web Resource ◽

Cancer Types

ABSTRACTDysregulation of gene expression plays an important role in cancer development. Identifying transcriptional regulators, including transcription factors and chromatin regulators, that drive the oncogenic gene expression program is a critical task in cancer research. Genomic profiles of active transcriptional regulators from primary cancer samples are limited in the public domain. Here we present BART Cancer (bartcancer.org), an interactive web resource database to display the putative transcriptional regulators that are responsible for differentially regulated genes in 15 different cancer types in The Cancer Genome Atlas (TCGA). BART Cancer integrates over 10,000 gene expression profiling RNA-seq datasets from TCGA with over 7,000 ChIP-seq datasets from the Cistrome Data Browser database and the Gene Expression Omnibus (GEO). BART Cancer uses Binding Analysis for Regulation of Transcription (BART) for predicting the transcriptional regulators from the differentially expressed genes in cancer samples compared to normal samples. BART Cancer also displays the activities of over 900 transcriptional regulators across cancer types, by integrating computational prediction results from BART and the Cistrome Cancer database. Focusing on transcriptional regulator activities in human cancers, BART Cancer can provide unique insights into epigenetics and transcriptional regulation in cancer, and is a useful data resource for genomics and cancer research communities.

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