Prognostic significance of FAM83D gene expression across human cancer types

The explosion of available microarray data on human cancer increases the urgency for developing methods for effectively sharing this data among clinical cancer investigators. Lack of a smooth interface between the databases and statistical analysis tools limits the potential benefits of sharing the publicly available microarray data. To facilitate the efficient sharing and use of publicly available microarray data among cancer investigators, we have built a BRB-ArrayTools Data Archive including over one hundred human cancer microarray projects for 28 cancer types. Expression array data and clinical descriptors have been imported into BRB-ArrayTools and are stored as BRB-ArrayTools project folders on the archive. The data archive can be accessed from: http://www.linus.nci.nih.gov/~brb/DataArchive.html Our BRB-ArrayTools data archive and GEO importer represent ongoing efforts to provide effective tools for efficiently sharing and utilizing human cancer microarray data.

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Effects of TP53 mutational status on gene expression patterns across 10 human cancer types

The Journal of Pathology ◽

10.1002/path.4321 ◽

2014 ◽

Vol 232 (5) ◽

pp. 522-533 ◽

Cited By ~ 40

Author(s):

Neha Parikh ◽

Susan Hilsenbeck ◽

Chad J Creighton ◽

Tajhal Dayaram ◽

Ryan Shuck ◽

...

Keyword(s):

Gene Expression ◽

Human Cancer ◽

Expression Patterns ◽

Gene Expression Patterns ◽

Mutational Status ◽

Cancer Types ◽

Tp53 Mutational Status

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Discretization of Gene Expression Data Unmasks Molecular Subgroups Recurring in Different Human Cancer Types

PLoS ONE ◽

10.1371/journal.pone.0161514 ◽

2016 ◽

Vol 11 (8) ◽

pp. e0161514 ◽

Cited By ~ 2

Author(s):

Manfred Beleut ◽

Robert Soeldner ◽

Mark Egorov ◽

Rolf Guenther ◽

Silvia Dehler ◽

...

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Human Cancer ◽

Expression Data ◽

Molecular Subgroups ◽

Cancer Types

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Phosphodiesterase 4D Depletion/Inhibition Exerts Anti-Oncogenic Properties in Hepatocellular Carcinoma

Cancers ◽

10.3390/cancers13092182 ◽

2021 ◽

Vol 13 (9) ◽

pp. 2182

Author(s):

Federica Ragusa ◽

Nadia Panera ◽

Silvia Cardarelli ◽

Marco Scarsella ◽

Marzia Bianchi ◽

...

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Human Cancer ◽

Specific Inhibitor ◽

Protein Activity ◽

Over Expression ◽

Insulin Growth Factor ◽

Cancer Types ◽

Phosphodiesterase 4D ◽

Hcc Cells

Isoform D of type 4 phosphodiesterase (PDE4D) has recently been associated with several human cancer types with the exception of human hepatocellular carcinoma (HCC). Here we explored the role of PDE4D in HCC. We found that PDE4D gene/protein were over-expressed in different samples of human HCCs compared to normal livers. Accordingly, HCC cells showed higher PDE4D activity than non-tumorigenic cells, accompanied by over-expression of the PDE4D isoform. Silencing of PDE4D gene and pharmacological inhibition of protein activity by the specific inhibitor Gebr-7b reduced cell proliferation and increased apoptosis in HCC cells, with a decreased fraction of cells in S phase and a differential modulation of key regulators of cell cycle and apoptosis. PDE4D silencing/inhibition also affected the gene expression of several cancer-related genes, such as the pro-oncogenic insulin growth factor (IGF2), which is down-regulated. Finally, gene expression data, available in the CancerLivER data base, confirm that PDE4D over-expression in human HCCs correlated with an increased expression of IGF2, suggesting a new possible molecular network that requires further investigations. In conclusion, intracellular depletion/inhibition of PDE4D prevents the growth of HCC cells, displaying anti-oncogenic effects. PDE4D may thus represent a new biomarker for diagnosis and a potential adjuvant target for HCC therapy.

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Molecular correlates and therapeutic targets in T cell-inflamed versus non-T cell-inflamed tumors across cancer types

10.1101/773127 ◽

2019 ◽

Author(s):

Riyue Bao ◽

Jason J. Luke

Keyword(s):

Gene Expression ◽

T Cell ◽

Hedgehog Signaling ◽

Human Cancer ◽

Expression Patterns ◽

Gene Expression Signature ◽

Gene Signature ◽

Type I ◽

Tumor Type ◽

Cancer Types

AbstractThe T cell-inflamed tumor microenvironment, characterized by CD8 T cells and type I/II interferon transcripts, is an important cancer immunotherapy biomarker. Tumor mutational profile may also dictate response with some oncogenes (i.e. WNT/β-catenin) known to mediate immuno-suppression. Building on these observations we performed a multi-omic analysis of human cancer correlating the T cell-inflamed gene expression signature with the somatic mutanome and transcriptome for different immune phenotypes, by tumor type and across cancers. Strong correlations were noted between mutations in oncogenes and non-T cell-inflamed tumors with examples including IDH1 and GNAQ as well as less well-known genes including KDM6A, CD11c and genes with unknown functions. Conversely, we observe many genes associating with the T cell-inflamed phenotype including VHL and PBRM1, among others. Analyzing gene expression patterns, we identify oncogenic mediators of immune exclusion broadly active across cancer types including HIF1A and MYC. Novel examples from specific tumors include sonic hedgehog signaling in ovarian cancer or hormone signaling and novel transcription factors across multiple tumors. Using network analysis, somatic and transcriptomic events were integrated, demonstrating that most non-T cell-inflamed tumors are influenced by multiple pathways. Validating these analyses, we observe significant inverse relationships between protein levels and the T cell-inflamed gene signature with examples including NRF2 in lung, ERBB2 in urothelial and choriogonadotropin in cervical cancer. Finally, we integrate available databases for drugs that might overcome or augment the identified mechanisms. These results nominate molecular targets and drugs potentially available for immediate translation into clinical trials for patients with cancer.

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Prognostic Landscape of Tumor-Infiltrating T and B Cells in Human Cancer

Frontiers in Immunology ◽

10.3389/fimmu.2021.731329 ◽

2021 ◽

Vol 12 ◽

Author(s):

Ming Zheng ◽

Yi-Ming Li ◽

Zhen-Yu Liu ◽

Xin Zhang ◽

Yinghui Zhou ◽

...

Keyword(s):

B Cells ◽

Chemokine Receptors ◽

Human Cancer ◽

Histological Grade ◽

Critical Role ◽

Prognostic Significance ◽

Tumor Infiltrating Lymphocytes ◽

T And B Cells ◽

Protein Levels ◽

Cancer Types

Recently, immunotherapy targeting tumor-infiltrating lymphocytes (TILs) has emerged as a critical and promising treatment in several types of cancer. However, not all cancer types have been tested in immunotherapeutic trials, and different patients and cancer types may have unpredictable clinical outcomes. This situation has created a particular exigency for analyzing the prognostic significance of tumor-infiltrating T cells (TIL-T) and B cells (TIL-B) across different cancer types. To address the critical role of TILs, the abundances of TIL-T and TIL-B cells, as determined by the protein levels of LCK and CD20, were analyzed across heterogeneous human malignancies. TIL-T and TIL-B cells showed varying prognostic significances across heterogeneous cancer types. Additionally, distinct distributions of TIL-T and TIL-B cells were observed in different cancer and tumor microenvironment (TME) subtypes. Next, we analyzed the cellular context for the TME communication network involving the well-acknowledgeable chemokine receptors of TIL-T and TIL-B cells, implying the functional interactions with TME. Additionally, these chemokine receptors, expressed by TIL-T and TIL-B cells, were remarkably correlated with the levels of TIL-T or TIL-B cell infiltrations across nearly all the cancer types, indicating these chemokine receptors as universal targets for up- and down-regulating the TIL-T and TIL-B cells. Lastly, we provide the prognostic landscape of TIL-T and TIL-B cells across 30 cancer types and the subgroups defined by gender, histopathology, histological grade, therapeutic approach, drug, and TME subtype, which are intended to be a resource to fuel the investigations of TILs, with important implications for cancer immunotherapy.

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Upregulated KPNA2 promotes hepatocellular carcinoma progression and indicates prognostic significance across human cancer types

Acta Biochimica et Biophysica Sinica ◽

10.1093/abbs/gmz003 ◽

2019 ◽

Vol 51 (3) ◽

pp. 285-292 ◽

Cited By ~ 8

Author(s):

Xinggang Guo ◽

Zhiheng Wang ◽

Jianing Zhang ◽

Qingguo Xu ◽

Guojun Hou ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Human Cancer ◽

Prognostic Significance ◽

Cancer Types

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Molecular correlates and therapeutic targets in T cell-inflamed versus non-T cell-inflamed tumors across cancer types

Genome Medicine ◽

10.1186/s13073-020-00787-6 ◽

2020 ◽

Vol 12 (1) ◽

Author(s):

Riyue Bao ◽

Daniel Stapor ◽

Jason J. Luke

Keyword(s):

Gene Expression ◽

T Cell ◽

Hedgehog Signaling ◽

Human Cancer ◽

Expression Patterns ◽

Gene Expression Signature ◽

Type I ◽

Tumor Type ◽

Sonic Hedgehog Signaling ◽

Cancer Types

Abstract Background The T cell-inflamed tumor microenvironment, characterized by CD8 T cells and type I/II interferon transcripts, is an important cancer immunotherapy biomarker. Tumor mutational burden (TMB) may also dictate response, and some oncogenes (i.e., WNT/β-catenin) are known to mediate immunosuppression. Methods We performed an integrated multi-omic analysis of human cancer including 11,607 tumors across multiple databases and patients treated with anti-PD1. After adjusting for TMB, we correlated the T cell-inflamed gene expression signature with somatic mutations, transcriptional programs, and relevant proteome for different immune phenotypes, by tumor type and across cancers. Results Strong correlations were noted between mutations in oncogenes and tumor suppressor genes and non-T cell-inflamed tumors with examples including IDH1 and GNAQ as well as less well-known genes including KDM6A, CD11c, and genes with unknown functions. Conversely, we observe genes associating with the T cell-inflamed phenotype including VHL and PBRM1. Analyzing gene expression patterns, we identify oncogenic mediators of immune exclusion across cancer types (HIF1A and MYC) as well as novel examples in specific tumors such as sonic hedgehog signaling, hormone signaling and transcription factors. Using network analysis, somatic and transcriptomic events were integrated. In contrast to previous reports of individual tumor types such as melanoma, integrative pan-cancer analysis demonstrates that most non-T cell-inflamed tumors are influenced by multiple signaling pathways and that increasing numbers of co-activated pathways leads to more highly non-T cell-inflamed tumors. Validating these analyses, we observe highly consistent inverse relationships between pathway protein levels and the T cell-inflamed gene expression across cancers. Finally, we integrate available databases for drugs that might overcome or augment the identified mechanisms. Conclusions These results nominate molecular targets and drugs potentially available for further study and potential immediate translation into clinical trials for patients with cancer.

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The histone code reader PHD finger protein 7 controls sex-linked disparities in gene expression and malignancy in Drosophila

Science Advances ◽

10.1126/sciadv.aaw7965 ◽

2019 ◽

Vol 5 (8) ◽

pp. eaaw7965 ◽

Cited By ~ 2

Author(s):

Cristina Molnar ◽

Jan Peter Heinen ◽

Jose Reina ◽

Salud Llamazares ◽

Emilio Palumbo ◽

...

Keyword(s):

Gene Expression ◽

Brain Tumors ◽

Quantitative Proteomics ◽

Human Cancer ◽

Malignant Neoplasms ◽

Male And Female ◽

Phd Finger ◽

Male Predominance ◽

Finger Protein ◽

Cancer Types

The notable male predominance across many human cancer types remains unexplained. Here, we show that Drosophila l(3)mbt brain tumors are more invasive and develop as malignant neoplasms more often in males than in females. By quantitative proteomics, we have identified a signature of proteins that are differentially expressed between male and female tumor samples. Prominent among them is the conserved chromatin reader PHD finger protein 7 (Phf7). We show that Phf7 depletion reduces sex-dependent differences in gene expression and suppresses the enhanced malignant traits of male tumors. Our results identify potential regulators of sex-linked tumor dimorphism and show that these genes may serve as targets to suppress sex-linked malignant traits.

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Convolutional neural network for human cancer types prediction by integrating protein interaction networks and omics data

Scientific Reports ◽

10.1038/s41598-021-98814-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yi-Hsuan Chuang ◽

Sing-Han Huang ◽

Tzu-Mao Hung ◽

Xiang-Yu Lin ◽

Jung-Yu Lee ◽

...

Keyword(s):

Neural Network ◽

Gene Expression ◽

Cancer Diagnosis ◽

Protein Interaction ◽

Human Cancer ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Multiple Cancer ◽

Diagnosis And Prognosis ◽

Cancer Types

AbstractMany studies have proven the power of gene expression profile in cancer identification, however, the explosive growth of genomics data increasing needs of tools for cancer diagnosis and prognosis in high accuracy and short times. Here, we collected 6136 human samples from 11 cancer types, and integrated their gene expression profiles and protein–protein interaction (PPI) network to generate 2D images with spectral clustering method. To predict normal samples and 11 cancer tumor types, the images of these 6136 human cancer network were separated into training and validation dataset to develop convolutional neural network (CNN). Our model showed 97.4% and 95.4% accuracies in identification of normal versus tumors and 11 cancer types, respectively. We also provided the results that tumors located in neighboring tissues or in the same cell types, would induce machine make error classification due to the similar gene expression profiles. Furthermore, we observed some patients may exhibit better prognosis if their tumors often misjudged into normal samples. As far as we know, we are the first to generate thousands of cancer networks to predict and classify multiple cancer types with CNN architecture. We believe that our model not only can be applied to cancer diagnosis and prognosis, but also promote the discovery of multiple cancer biomarkers.

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