A Liquid-Liquid Phase Separation-Related Gene Signature as Prognostic Biomarker for Epithelial Ovarian Cancer

Qiu, Yan; Pan, Min

doi:10.3389/fonc.2021.671892

Cited by 14 publications

(15 citation statements)

References 41 publications

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“…Currently, several bioinformatics signatures have been constructed in other tumors (27,28). Qiu et al constructed the signature of 11 LLPS-related genes in ovarian cancer by bioinformatics analysis, and accurately stratified the prognosis of ovarian cancer patients, among which patients in the high-risk group had significantly worse prognosis (29). And they found that LLPS-related genes were involved in several cancer-related pathways, such as MAPK signaling, cell cycle and DNA replication.…”

Section: Discussionmentioning

confidence: 99%

Single cell sequencing analysis and transcriptome analysis constructed the liquid-liquid phase separation(LLPS)-related prognostic model for endometrial cancer

2022

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BackgroundEndometrial cancer is one of the most common gynecological tumors in developed countries. Our understanding of the pathogenesis of endometrial cancer and the changes in the immune microenvironment are still unclear. It is necessary to explore new biomarkers to guide the diagnosis and treatment of endometrial cancer.MethodsThe GEO database was used to download the endometrial cancer single cell sequencing dataset GSE173682. The UCSC database was used to download transcriptome sequencing data. The validation set was the transcriptome dataset GSE119041, which was retrieved from the GEO database. On the DrLLPS website, liquid-liquid phase separation-related genes can be downloaded. Relevant hub genes were found using weighted co-expression network analysis and dimension reduction clustering analysis. Prognostic models were built using Lasso regression and univariate COX regression. Analyses of immune infiltration were employed to investigate the endometrial cancer immunological microenvironment. The expression of model genes in endometrial cancer was confirmed using a PCR test.ResultsWe created an LLPS-related predictive model for endometrial cancer by extensive study, and it consists of four genes: EIF2S2, SNRPC, PRELID1, and NDUFB9. Patients with endometrial cancer may be classified into high-risk and low-risk groups based on their risk scores, and those in the high-risk group had significantly worse prognoses (P<0.05). Additionally, there were notable variations in the immunological milieu between the groups at high and low risk. EIF2S2, SNRPC, PRELID1, and NDUFB9 were all up-regulated in endometrial cancer tissues, according to PCR results.ConclusionsOur study can provide a certain reference for the diagnosis and treatment of endometrial cancer.

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Section: Discussionmentioning

confidence: 99%

Single cell sequencing analysis and transcriptome analysis constructed the liquid-liquid phase separation(LLPS)-related prognostic model for endometrial cancer

2022

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“…Frontiers in Genetics frontiersin.org assess the effects of LLPS in the prediction of tumor prognosis in ovarian epithelial cancer and lung cancer (Qiu et al, 2021;Zhuge et al, 2021). These two studies showed that LCGs were useful in distinguishing subgroups in which one group had a better prognosis, while the other had a worse prognosis.…”

Section: Discussionmentioning

confidence: 99%

Comprehensive analysis of liquid-liquid phase separation-related genes in prediction of breast cancer prognosis

et al. 2022

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Objective: Liquid-liquid phase separation (LLPS) is a functional unit formed by specific molecules. It lacks a membrane and has been reported to play a crucial role in tumor drug resistance and growth by regulating gene expression and drug distribution. However, whether LLPS could be used to predict cancer prognosis was not clear. This study aimed to construct a prognostic model for breast cancer based on LLPS-correlated genes (LCGs).Methods: LCGs were identified using the PhaSepDB, gene expression profile and clinical characteristics of breast cancer were obtained from TCGA and cBioportal. The PanCancer Atlas (TCGA) cohort was used as the training cohort to construct the prognostic model, while the Nature 2012 and Nat Commun 2016 (TCGA) cohort and GEO data were used as test cohort to perform external verification. Data analysis was performed with R4.2.0 and SPSS20.0.Results: We identified 140 prognosis-related LCGs (pLCGs) (p< 0.01) in all cohorts, 240 pLCGs (p< 0.01) in the luminal cohort, and 28 pLCGs (p< 0.05) in the triple-negative breast cancer (TNBC) cohort. Twelve genes in all cohorts (training cohort: 5/10-year ROC values were 0.76 and 0.77; verification cohort: 5/10-year ROC values were 0.61 and 0.58), eight genes in the luminal cohort (training cohort: 5/10-year ROC values were 0.79 and 0.75; verification cohort: 5/10-year ROC values were 0.62 and 0.62), and four genes in the TNBC cohort (training cohort: 5/10-year ROC values were 0.73 and 0.79; verification cohort: 5/10-year ROC values were 0.55 and 0.54) were screened out to construct the prognostic prediction model. The 17-gene risk-score was constructed in all cohorts (1/3/5-year ROC values were 0.88, 0.83, and 0.81), and the 11-gene risk-score was constructed in the luminal cohort (1/3/5-year ROC values were 0.67, 0.85 and 0.84), and the six-gene risk-score was constructed in the TNBC cohort (1/3/5-year ROC value were 0.87, 0.88 and 0.81). Finally, the risk-score and clinical factors were applied to construct nomograms in all cohorts (1/3/5-year ROC values were 0.89, 0.79 and 0.75, C-index = 0.784), in the luminal cohort (1/3/5-year ROC values were 0.84, 0.83 and 0.85, C-index = 0.803), and in the TNBC cohort (1/3/5-year ROC values were 0.95, 0.84 and 0.77, C-index = 0.847).Discussion: This study explored the roles of LCGs in the prediction of breast cancer prognosis.

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“…The potential prognostic value of LLPS-related molecular has also received increasing attention. Qiu et al constructed a LLPS-related GS as a prognostic tool for epithelial ovarian cancer [ 17 ]. Our analysis might firstly develop the novel LLPS-related GS for predicting the prognosis of LGG.…”

Section: Discussionmentioning

confidence: 99%

“…It was shown that the aberration of liquid-liquid phase separation involves in many human diseases, including neurodegenerative diseases [ 14 ] and cancer [ 15 ]. Previous studies suggested that LLPS-related gene signature can be used as a prognostic marker for hepatocellular carcinoma [ 16 ] and ovarian cancer [ 17 ]. Thus, we hypothesized that LLPS-related genes may be potential prognostic markers that could contribute to the further classification of LGG.…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Liquid-Liquid Phase Separation-Related Gene Signature as Prognostic Biomarker for Low-Grade Gliomas

et al. 2022

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Aim. To explore whether the liquid-liquid phase separation- (LLPS-) related genes were potential prognostic markers that could contribute to the further classification of low-grade gliomas (LGGs). Methods. The LLPS-related genes were subjected to functional enrichment analysis. The univariable, least absolute shrinkage and selection operator, and multivariable stepwise Cox regression analyses were performed to develop an LLPS-related gene signature (GS) in the discovery data set. The biological characteristics of the high-risk LGG were explored using gene set enrichment analysis. Two independent external data sets were used to validate the LLPS-related GS. Results. LLPS-related genes are involved in multiple important cancer-related biological processes and pathways in LGG. Nine LLPS-related genes were identified to construct the LLPS-related GS, which was significantly associated with the prognosis of LGG patients. The LLPS-related GS could successfully divide patients with LGG into high- and low-risk groups, and the high-risk group showed a poorer prognosis than the low-risk group. Furthermore, the LLPS-related GS was independent of IDH and 1p19q status. Several cancer-related pathways may be more active in high-risk LGGs, such as IL6 JAK STAT3 signaling pathway. The LLPS-related GS was successfully validated with two independent external data sets. Conclusion. We developed and validated a novel LLPS-related GS for risk stratification of LGG. Our findings may provide more precise management for LGGs and a useful reference for LLPS mechanism to link LGG studies.

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A Liquid-Liquid Phase Separation-Related Gene Signature as Prognostic Biomarker for Epithelial Ovarian Cancer

Cited by 14 publications

References 41 publications

Single cell sequencing analysis and transcriptome analysis constructed the liquid-liquid phase separation(LLPS)-related prognostic model for endometrial cancer

Single cell sequencing analysis and transcriptome analysis constructed the liquid-liquid phase separation(LLPS)-related prognostic model for endometrial cancer

Comprehensive analysis of liquid-liquid phase separation-related genes in prediction of breast cancer prognosis

Development and Validation of a Liquid-Liquid Phase Separation-Related Gene Signature as Prognostic Biomarker for Low-Grade Gliomas

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