TRPs in Ovarian Serous Cystadenocarcinoma: The Expression Patterns, Prognostic Roles, and Potential Therapeutic Targets

Zhang, Cheng; Xu, Cong; Ma, Chuanshun; Zhang, Qinghua; Bu, Siyuan; Zhang, Daolai; Yu, Liting; Wang, Hongmei

doi:10.3389/fmolb.2022.915409

Cited by 3 publications

(1 citation statement)

References 55 publications

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“…Transient receptor potential (TRP) channels are important mediators of sensory perception with significant effects on cellular functions and signalling pathways. Based on sequence homology, the mammalian TRP channel superfamily is classified the majority of TRPs are more selective for calcium, which is considered the most important second messenger involved in physiology and pathological events, such as cancer progression ( Morelli and Amantini, 2022 ; Zhang et al, 2022 ). Transient receptor potential melastatin 4 (TRPM4) channel is a Ca 2+ -activated nonselective cation channel, belonging to the TRPM family of TRP channels, highly selective for monovalent cations such as Na + and K + , but impermeable to anions and divalent cations including Ca 2+ ( Launay et al, 2002 ; Guo et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

TRPM4 and TRPV2 are two novel prognostic biomarkers and promising targeted therapy in UVM

Wang

Qiao

Liang

et al. 2022

Front. Mol. Biosci.

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Uveal melanoma (UVM) is the most common primary intraocular malignancy tumor in adults. Almost 50% of UVM patients develop metastatic disease, and is usually fatal within 1 year. However, the mechanism of etiology remains unclear. The lack of prognostic, diagnostic and therapeutic biomarkers is a main limitation for clinical diagnosis and treatment. The transient receptor potential (TRP) channels play important roles in the occurrence and development of tumors, which may have the potential as a therapeutic target for UVM. This current study aimed to identify the potential effect and function of the TRPs that could provide survival prediction and new insight into therapy for UVM. Based on the transcriptome data and potential key genes of UVM were screened using the Cancer Genome Atlas (TCGA) databases, Gene expression analysis showed the expression of TRPM4, TRPV2 and other TRPs was high levels in UVM. Using survival analysis, we screened out that the high expression of TRPM4 and TRPV2 was negatively correlated with the prognosis of UVM patients. Cox regression analysis and functional enrichment analysis further indicated that TRPM4 and TRPV2 were the most convincing therapeutic targets of UVM, and the majority of genes involved in ferroptosis pathways in UVM showed positively correlated with the expression levels of TRPM4 and TRPV2. In conclusion, TRPM4 and TRPV2 were considered as two novel prognostic biomarkers and a promising targeted therapy in UVM.

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Section: Introductionmentioning

confidence: 99%

TRPM4 and TRPV2 are two novel prognostic biomarkers and promising targeted therapy in UVM

Wang

Qiao

Liang

et al. 2022

Front. Mol. Biosci.

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Transcriptomic Gene Network Profiling and Weak Signal Detection for Prediction of Ovarian Cancer Occurrence, Survival, and Severity by Integrating Bulk and Single-cell RNAseq Data

Li,

Sardiu,

Koestler

et al. 2023

Preprint

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BackgroundOvarian cancer (OC) is a significant gynecological malignancy characterized by its high mortality rate, poor long-term survival rate, and late-stage diagnosis. OC is the 5th leading cause of cancer death among woman and counts 2.1% of all cancer death. OC survival rates are much lower than other cancers that affect woman. Its 5-year survival rate is less than 50%. Only ∼17% of OC patients are diagnosed within the early stage. The majority are diagnosed at an advanced stage, making early detection and effective treatment critical challenges. Currently, the identified OC predictive genes are still very sparse, resulting in pool prognostic performance. There exists unmet needs to identify novel prognostic gene biomarkers for OC occurrence, survival, and clinical stages to promote the likelihood of survival and to perform optimal treatments or therapeutic strategies at the earliest stage possible.MethodsPrevious RNAseq analysis on OC focused on detecting differentially expressed (DE) genes only. Many genes, although having weak marginal differential effects, may still exude strong predictive effects on disease outcomes though regulating other DE genes. In this work, we employed a new machine learning method, netLDA, to detect such predictive coregulating genes with weak marginal DE effects for predicting OC occurrence, 5-year survival, and clinical stage. The netLDA detects predictive gene networks (PGN) containing strong DE genes as hub genes and detects coregulating weak genes within the PGNs. The network structures of the detected PGNs along with the strong and weak genes therein are then used in outcome prediction on test datasets.ResultsWe identified different sets of signature genes for OC occurrence, survival, and clinical stage. Previously identified prognostic genes, such asEPCAM, UBE2C, CHD1L, TP53,CD24,WFDC2, andFANCI,were confirmed. We also identified novel predictive coregulating weak genes includingGIGYF2, GNPAT, RAD54L, andELL.Many of the detected predictive gene networks and coregulating weak genes therein overlapped with OC-related biological pathways such as KEGGtight junction, ribosome, andcell cyclepathways. The detection and incorporation of the gene networks and weak genes significantly improved the prediction performance. Cellular mapping of selected feature genes using single-cell RNAseq data further revealed the heterogeneous expression distributions of the signature genes on different cell types.ConclusionsWe established a transcriptomic gene network profile for OC prediction. The novel genes detected provide new targets for early diagnostics and new drug development for OC.

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Calcium signals and potential therapy targets in ovarian cancer (Review)

Deng,

Fu,

Zhao

et al. 2023

Int J Oncol

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TRPs in Ovarian Serous Cystadenocarcinoma: The Expression Patterns, Prognostic Roles, and Potential Therapeutic Targets

Cited by 3 publications

References 55 publications

TRPM4 and TRPV2 are two novel prognostic biomarkers and promising targeted therapy in UVM

TRPM4 and TRPV2 are two novel prognostic biomarkers and promising targeted therapy in UVM

Transcriptomic Gene Network Profiling and Weak Signal Detection for Prediction of Ovarian Cancer Occurrence, Survival, and Severity by Integrating Bulk and Single-cell RNAseq Data

Calcium signals and potential therapy targets in ovarian cancer (Review)

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