PROGgene: gene expression based survival analysis web application for multiple cancers

Goswami, Chirayu; Nakshatri, Harikrishna

doi:10.1186/2043-9113-3-22

Cited by 130 publications

(120 citation statements)

References 20 publications

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“…In contrast, TMEPAI acts as a tumor suppressor in prostate cancer (8,9). To find out the effects of TMEPAI on the survival of patients with MM, a fetal hematological malignancy, we analyzed the PROGgene database from http://watson.compbio.iupui.edu/chirayu/p roggene/database/index.php (20), which curates a large size of reported data on various cancer genes and associated patient survival. There was one confirmative dataset (GSE2658) (21) regarding the prognostic value of TMEPAI expression and MM patients.…”

Section: Overexpression Of Tmepai Induces MM Cell Apoptosismentioning

confidence: 99%

“…The association of TMEPAI expression with the overall survival of MM patients was analyzed using the PROGgene database (http://watson.compbio.iupui.edu/chirayu/ proggene/database/index.php) (20). The TMEPAI expression profile was analyzed using the DNA microarray on the typical 60 cancer cell lines from National Cancer Institute (NCI 60-cell line) (https://www.ncbi.nlm.nih.gov/sites/GDSb rowser?acc=GDS4296).…”

Section: Tmepai Analyses Based On the Public Gene Expression Databasesmentioning

confidence: 99%

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The transmembrane protein TMEPAI induces myeloma cell apoptosis by promoting degradation of the c-Maf transcription factor

Liu

et al. 2018

Journal of Biological Chemistry

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Transmembrane prostate androgen-induced protein (TMEPAI, also called prostate transmembrane protein, androgen induced 1 [PMEPA1]), is a type I transmembrane (TM) protein, but its cellular function is largely unknown. Here, studying factors influencing the stability of c-Maf, a critical transcription factor in multiple myeloma (MM), we found that TMEPAI induced c-Maf degradation. We observed that TMEPAI recruited neural precursor cell expressed, developmentally down-regulated 4 (NEDD4), a WW domain-containing ubiquitin ligase, to c-Maf, leading to its degradation through the proteasomal pathway. Further investigation revealed that TMEPAI interacts with NEDD4 via its conserved PY motifs. Alanine substitution or deletion of these motifs abrogated the TMEPAI complex formation with NEDD4, resulting in failed c-Maf degradation. Functionally, TMEPAI suppressed the transcriptional activity of c-Maf. Of note, increased TMEPAI expression was positively associated with the overall survival of MM patients. Moreover, (1). In addition to androgen, TMEPAI expression is also stimulated by some growth factors such as epidermal growth factor (EGF) and transforming growth factor-β (TGF-β) (2). The expression profiling found that TMEPAI is overexpressed in solid cancers including prostate cancer , lung (3,4), breast (3,5) and ovarian cancers (6), and rental cell carcinoma (7), but it is not detectable in liquid cancers such as leukemia and lymphoma samples (7). The specific expression pattern suggests that TMEPAI might function differently upon specific cancer types. For example, TMEPAI displays as a tumor suppressor in prostate cancer progression because silence of TMEPAI leads to accelerated proliferation of prostate cancer cells (8) while expression of TMEPAI inhibits prostate cancer cell growth (9) and prevents its bone metastasis (10). However, TMEPAI acts as an oncogene and promotes proliferation and survival of lung and breast cancers. TMEPAI mediates the degradation of receptor-activated SMAD and activates non-canonical PI3K/AKT signaling transduction thus promoting TGF-β-dependent cell growth and metastasis of triple negative breast cancer (3). In lung cancer cells, TMEPAI not only increases cancer cell proliferation, migration and invasion (11,12) but also enhances tumorigenicity and the epithelial-mesenchymal transition (EMT) of lung cancer cells (4). These actions were associated with TMEPAI-induced TGF-β degradation in lysosomes and TMEPAI-induced reactive oxygen species and insulin receptor substrate-1 (4). In contrast, knockdown of TMEPAI enhances Smad2 phosphorylation and significantly suppressed lung cancer cell proliferation in the presence of TGF-β. All the above results collectively suggest that TMEPAI can promote or suppress cancer cell proliferation and tumor growth depending on the cancer types but the TGF-β signaling pathway is a key factor.Hematological malignancies are a collection of blood cancers including leukemia, lymphoma and multiple myeloma in which TMEPAI is less expressed (7), but the fun...

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Section: Overexpression Of Tmepai Induces MM Cell Apoptosismentioning

confidence: 99%

Section: Tmepai Analyses Based On the Public Gene Expression Databasesmentioning

confidence: 99%

The transmembrane protein TMEPAI induces myeloma cell apoptosis by promoting degradation of the c-Maf transcription factor

Liu

et al. 2018

Journal of Biological Chemistry

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“…The results were obtained using R package 'survival' and 'survAUC'. 6 The GIANT provides grouped variable selection methods (least absolute shrinkage and selection operator; LASSO, Elastic Net Regularization; Elastic Net, Network-Regularized high-dimensional Cox-regression; Net) by using 'coxnet' package in R. In the Net analysis, we transformed the topologic pathway information of large databases (KEGG, Biocarta, HumanCyc, Reactome, Panther, and NCI) into a gene network matrix by using 'graphite' in R. Users can set the mixing parameter α, which decides the balance between LASSO and Ridge regression. All grouped variable selection used "leave-one-out" method for crossvalidation.…”

Section: Significant Genes Of Specific Cancermentioning

confidence: 99%

“…For instance, SurvExpress, PROGgene, and PrognoScan provide statistical prognostic significance by using mRNA expression data [5][6][7]. However, the survival analysis results are prone to be associated with following limitations: (1) the tools use mRNA expression as a simple categorical value to provide Kaplan-Meier curve in all patients regardless of their characteristics.…”

Section: Introductionmentioning

confidence: 99%

GIANT: an online resource for comprehensive survival analysis in pan-cancer from The Cancer Genome Atlas (Preprint)

Han¹,

Goh²,

Jeong³

et al. 2018

Preprint

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“…However, the detection of the expression of a single gene does not effectively characterize the expression of a gene cluster composed of tens or hundreds of genes. It has been demonstrated that prognostic information from multi-gene signatures, including network modules, can reduce the heterogeneity of diseases with greater accuracy than data of a single gene (6). For example, a classifying system for breast cancer profiles was constructed based on 70 genes in a study by van de Vijver et al (7).…”

Section: Introductionmentioning

confidence: 99%

Identification of breast cancer prognostic modules based on weighted protein-protein interaction networks

Bai

et al. 2017

Oncology Letters

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Abstract. Breast cancer is one of the leading causes of mortality in females. A number of prognostic markers have been identified, including single genes, multi-gene signatures and network modules; however, the robustness of these prognostic markers is insufficient. Thus, the present study proposed a more robust method to identify breast cancer prognostic modules based on weighted protein-protein interaction networks, by integrating four sets of disease-associated expression profiles. Three identified prognostic modules were closely associated with prognosis-associated functions and survival time, as determined by Cox regression and Kaplan-Meier survival analyses. The robustness of these modules was verified with an independent profile from another platform. Genes from these modules may be useful as breast cancer prognostic markers. The prognostic modules could be used to determine the prognoses of patients with breast cancer and characterize patient recovery. IntroductionBreast cancer is one of the leading causes of female mortality (1-3). Prognosis reflects the outlook and chance of recovery from breast cancer. It is therefore critical to acquire prognostic information that represents the physical condition of the patients. Understanding prognostic information assists in increasing the survival rate and prolongs the life expectancy of patients (4).With the advent of microarray and next-generation sequencing technology, an increased volume of genomic data is available, including data relevant to breast cancer prognosis. In recent decades, a number of prognostic marker identification methods based on a single gene have been proposed (5). However, the detection of the expression of a single gene does not effectively characterize the expression of a gene cluster composed of tens or hundreds of genes. It has been demonstrated that prognostic information from multi-gene signatures, including network modules, can reduce the heterogeneity of diseases with greater accuracy than data of a single gene (6). For example, a classifying system for breast cancer profiles was constructed based on 70 genes in a study by van de Vijver et al (7). A treatment response prediction method for patients with breast cancer was developed based on 64 genes by Pawitan et al (8). The 21-gene signature has now been intensively studied and is widely used in clinical decisions regarding breast cancer (9).Prognostic marker identification methods have the problem of low robustness (10), and prognostic information obtained from certain patients may not be applicable to other patients (11). Thus, the present study aimed to develop a method to identify robust prognostic modules based on four breast cancer expression profiles and protein-protein interaction networks (PPINs). Experimental detection based on these prognostic modules could aid clinicians in the diagnosis of patients who are at risk of developing malignant disease, and allow the provision of prevention and treatment for these patients as early as possible. Materials and methodsExpressio...

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PROGgene: gene expression based survival analysis web application for multiple cancers

Cited by 130 publications

References 20 publications

The transmembrane protein TMEPAI induces myeloma cell apoptosis by promoting degradation of the c-Maf transcription factor

The transmembrane protein TMEPAI induces myeloma cell apoptosis by promoting degradation of the c-Maf transcription factor

GIANT: an online resource for comprehensive survival analysis in pan-cancer from The Cancer Genome Atlas (Preprint)

Identification of breast cancer prognostic modules based on weighted protein-protein interaction networks

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