Analysis of Gene Expression in Bladder Cancer: Possible Involvement of Mitosis and Complement and Coagulation Cascades Signaling Pathway

Liu, Ying; Xiong, Shenghua; Liu, Shiying; Chen, Jie; Yang, Hao; Liu, Gang; Li, Gengmi

doi:10.1089/cmb.2019.0237

Cited by 29 publications

(29 citation statements)

References 34 publications

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“…These results underline the potential of a transcriptional analysis to categorize clinical outcome. While most of the genes predicting outcome of CM have already been described as prognostic factors in other malignancies, such as CENPK [41][42][43][44][45][46] , INHA [47][48][49][50] , RPS6KL1 51 , CASP3 (Caspase 3) 52 , SERPINB1 (Serpin Family B Member 1) 53 , USP33 (Ubiquitin Specific Peptidase 33) [54][55][56][57][58][59] , TRPV4 60 and ACO2 61 , some of the predicting factors have not yet been associated to cancer prognosis, such as GPN1, SNRNP48, AAR2 and SNORA73B. Among the mentioned factors, GPN1 was the gene with the highest correlation coefficient with good clinical outcome in CM.…”

Section: Discussionmentioning

confidence: 99%

Transcriptional characterization of conjunctival melanoma identifies the cellular tumor microenvironment and prognostic gene signatures

Wolf¹,

Auw-Haedrich²,

Schlecht³

et al. 2020

Sci Rep

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This study characterizes the transcriptome and the cellular tumor microenvironment (TME) of conjunctival melanoma (CM) and identifies prognostically relevant biomarkers. 12 formalin-fixed and paraffin-embedded CM were analyzed by MACE RNA sequencing, including six cases each with good or poor clinical outcome, the latter being defined by local recurrence and/or systemic metastases. Eight healthy conjunctival specimens served as controls. The TME of CM, as determined by bioinformatic cell type enrichment analysis, was characterized by the enrichment of melanocytes, pericytes and especially various immune cell types, such as plasmacytoid dendritic cells, natural killer T cells, B cells and mast cells. Differentially expressed genes between CM and control were mainly involved in inhibition of apoptosis, proteolysis and response to growth factors. POU3F3, BIRC5 and 7 were among the top expressed genes associated with inhibition of apoptosis. 20 genes, among them CENPK, INHA, USP33, CASP3, SNORA73B, AAR2, SNRNP48 and GPN1, were identified as prognostically relevant factors reaching high classification accuracy (area under the curve: 1.0). The present study provides new insights into the TME and the transcriptional profile of CM and additionally identifies new prognostic biomarkers. These results add new diagnostic tools and may lead to new options of targeted therapy for CM.

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Section: Discussionmentioning

confidence: 99%

Transcriptional characterization of conjunctival melanoma identifies the cellular tumor microenvironment and prognostic gene signatures

Wolf¹,

Auw-Haedrich²,

Schlecht³

et al. 2020

Sci Rep

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“…In addition to possible changes in angiogenesis and cell adhesion molecules, complement and coagulation cascades were also affected in gastric cancer, which might participate in tumor progression and prognosis. Increasing evidence has indicated that complement and coagulation cascades were significantly involved in the signaling pathway in gallbladder cancer [ 51 ], clear cell renal cell carcinoma [ 52 ], small-cell lung cancer [ 53 ], epithelial ovarian cancer [ 54 ], bladder cancer [ 55 ], and head and neck cancer [ 56 ]. In gastric cancer, Gu et al once pointed that complement and coagulation cascades were significantly enriched pathways [ 57 ].…”

Section: Discussionmentioning

confidence: 99%

Progressive and Prognostic Performance of an Extracellular Matrix-Receptor Interaction Signature in Gastric Cancer

et al. 2020

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The role of an extracellular matrix- (ECM-) receptor interaction signature has not been fully clarified in gastric cancer. This study performed comprehensive analyses on the differentially expressed ECM-related genes, clinicopathologic features, and prognostic application in gastric cancer. The differentially expressed genes between tumorous and matched normal tissues in The Cancer Genome Atlas (TCGA) and validation cohorts were identified by a paired t -test. Consensus clusters were built to find the correlation between clinicopathologic features and subclusters. Then, the least absolute shrinkage and selection operator (lasso) method was used to construct a risk score model. Correlation analyses were made to reveal the relation between risk score-stratified subgroups and clinicopathologic features or significant signatures. In TCGA (26 pairs) and validation cohort (134 pairs), 25 ECM-related genes were significantly highly expressed and 11 genes were downexpressed in gastric cancer. ECM-based subclusters were slightly related to clinicopathologic features. We constructed a risk score model = 0.081 ∗ log 2 CD 36 + 0.043 ∗ log 2 COL 5 A 2 + 0.001 ∗ log 2 ITGB 5 + 0.039 ∗ log 2 SDC 2 + 0.135 ∗ log 2 SV 2 B + 0.012 ∗ log 2 THBS 1 + 0.068 ∗ log 2 VTN + 0.023 ∗ log 2 VWF . The risk score model could well predict the outcome of patients with gastric cancer in both training ( n = 351 , HR: 1.807, 95% CI: 1.292-2.528, P = 0.00046 ) and validation ( n = 300 , HR: 1.866, 95% CI: 1.347-2.584, P = 0.00014 ) cohorts. Besides, risk score-based subgroups were associated with angiogenesis, cell adhesion molecules, complement and coagulation cascades, TGF-beta signaling, and mismatch repair-relevant signatures ( P < 0.0001 ). By univariate (1.845, 95% CI: 1.382-2.462, P < 0.001 ) and multivariate (1.756, 95% CI: 1.284-2.402, P < 0.001 ) analyses, we regarded the risk score as an independent risk factor in gastric cancer. Our findings revealed that ECM compositions became accomplices in the tumorigenesis, progression, and poor survival of gastric cancer.

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“…Some of these pathways are already involved in cancer development. For example, hsa04610 might contribute to the progression of bladder cancer (Liu et al, 2020 ). The hsa05133 pathway is related to the hsa04610 pathway, so it also promotes bladder cancer formation.…”

Section: Experiments and Resultsmentioning

confidence: 99%

MI_DenseNetCAM: A Novel Pan-Cancer Classification and Prediction Method Based on Mutual Information and Deep Learning Model

et al. 2021

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The Pan-Cancer Atlas consists of original sequencing data from various sources, provides the opportunity to perform systematic studies on the commonalities and differences between diverse cancers. The analysis for the pan-cancer dataset could help researchers to identify the key factors that could trigger cancer. In this paper, we present a novel pan-cancer classification method, referred to MI_DenseNetCAM, to identify a set of genes that can differentiate all tumor types accurately. First, the Mutual Information (MI) was utilized to eliminate noise and redundancy from the pan-cancer datasets. Then, the gene data was further converted to 2D images. Next, the DenseNet model was adopted as a classifier and the Guided Grad-CAM algorithm was applied to identify the key genes. Extensive experimental results on the public RNA-seq data sets with 33 different tumor types show that our method outperforms the other state-of-the-art classification methods. Moreover, gene analysis further demonstrated that the genes selected by our method were related to the corresponding tumor types.

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Analysis of Gene Expression in Bladder Cancer: Possible Involvement of Mitosis and Complement and Coagulation Cascades Signaling Pathway

Cited by 29 publications

References 34 publications

Transcriptional characterization of conjunctival melanoma identifies the cellular tumor microenvironment and prognostic gene signatures

Transcriptional characterization of conjunctival melanoma identifies the cellular tumor microenvironment and prognostic gene signatures

Progressive and Prognostic Performance of an Extracellular Matrix-Receptor Interaction Signature in Gastric Cancer

MI_DenseNetCAM: A Novel Pan-Cancer Classification and Prediction Method Based on Mutual Information and Deep Learning Model

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