A novel model based on liquid‐liquid phase separation–Related genes correlates immune microenvironment profiles and predicts prognosis of lung squamous cell carcinoma

Zhuge, Lingdun; Zhang, Kun; Zhang, Zeliang; Guo, Wentao; Li, Yang; Bao, Qi

doi:10.1002/jcla.24135

Cited by 6 publications

(4 citation statements)

References 29 publications

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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%

“…For example, tau species that formed LLPS under cellular conditions could serve as intermediates for tau aggregate formation ( Wegmann et al, 2018 ); cAMP-dependent protein kinase (type I regulatory subunit) produced LLPS as part of their functional role in cAMP signaling to form biomolecular condensates enriched in cAMP and PKA activity, which was critical for effective cAMP compartmentation and played roles in atypical liver cancer ( Zhang et al, 2020 ); YAP protein formed a liquid aggregate in the nucleus and promoted the growth of breast cancer cells by inducing the transcription of oncogenes ( Li et al, 2021a ). In addition, some studies were reported to apply LCGs to construct multi-gene risk-scores to 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%

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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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Section: Discussionmentioning

confidence: 99%

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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“…Based on it, the membrane-less organelles can dynamically exchange components with surrounding structures to maintain a relatively stable intracellular environment. Moreover, the relevant studies continuously revealed the correlation of LLPS with tumorigenesis and tumor development [ 13 , 14 ]. Several studies had reported the importance of LLPS in PCa treatment and progression [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

A Liquid–Liquid Phase Separation-Related Index Associate with Biochemical Recurrence and Tumor Immune Environment of Prostate Cancer Patients

You

Chen

et al. 2023

IJMS

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To identify liquid–liquid phase separation (LLPS)-related molecular clusters, and to develop and validate a novel index based on LLPS for predicting the prognosis of prostate cancer (PCa) patients. We download the clinical and transcriptome data of PCa from TCGA and GEO database. The LLPS-related genes (LRGs) were extracted from PhaSepDB. Consensus clustering analysis was used to develop LLPS-related molecular subtypes for PCa. The LASSO cox regression analysis was performed to establish a novel LLPS-related index for predicting biochemical recurrence (BCR)-free survival (BCRFS). Preliminary experimental verification was performed. We initially identified a total of 102 differentially expressed LRGs for PCa. Three LLPS related molecular subtypes were identified. Moreover, we established a novel LLPS related signature for predicting BCRFS of PCa patients. Compared to low-risk patients in the training cohort, testing cohort and validating cohort, high-risk populations meant a higher risk of BCR and significantly poorer BCRFS. The area under receiver operating characteristic curve were 0.728, 0.762, and 0.741 at 1 year in the training cohort, testing cohort and validating cohort. Additionally, the subgroup analysis indicated that this index was especially suitable for PCa patients with age ≤ 65, T stage III-IV, N0 stage or in cluster 1. The FUS, which was the potential biomarker related to PCa liquid–liquid phase separation, was preliminarily identified and verified. This study successfully developed three LLPS-related molecular subtypes and identified a novel LLPS related molecular signature, which performed well in predicting BCRFS of PCa.

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“…However, no studies identified the useful genes for UC diagnosis prediction based on the comparison among different ML techniques 30 . It is highly recommended to combine or compare different methods to increase the accuracy of classification and prediction for the diagnosis research 31 – 33 .…”

Section: Introductionmentioning

confidence: 99%

Identification of useful genes from multiple microarrays for ulcerative colitis diagnosis based on machine learning methods

Zhang

Mao

Lau

et al. 2022

Sci Rep

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Ulcerative colitis (UC) is a chronic relapsing inflammatory bowel disease with an increasing incidence and prevalence worldwide. The diagnosis for UC mainly relies on clinical symptoms and laboratory examinations. As some previous studies have revealed that there is an association between gene expression signature and disease severity, we thereby aim to assess whether genes can help to diagnose UC and predict its correlation with immune regulation. A total of ten eligible microarrays (including 387 UC patients and 139 healthy subjects) were included in this study, specifically with six microarrays (GSE48634, GSE6731, GSE114527, GSE13367, GSE36807, and GSE3629) in the training group and four microarrays (GSE53306, GSE87473, GSE74265, and GSE96665) in the testing group. After the data processing, we found 87 differently expressed genes. Furthermore, a total of six machine learning methods, including support vector machine, least absolute shrinkage and selection operator, random forest, gradient boosting machine, principal component analysis, and neural network were adopted to identify potentially useful genes. The synthetic minority oversampling (SMOTE) was used to adjust the imbalanced sample size for two groups (if any). Consequently, six genes were selected for model establishment. According to the receiver operating characteristic, two genes of OLFM4 and C4BPB were finally identified. The average values of area under curve for these two genes are higher than 0.8, either in the original datasets or SMOTE-adjusted datasets. Besides, these two genes also significantly correlated to six immune cells, namely Macrophages M1, Macrophages M2, Mast cells activated, Mast cells resting, Monocytes, and NK cells activated (P < 0.05). OLFM4 and C4BPB may be conducive to identifying patients with UC. Further verification studies could be conducted.

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A novel model based on liquid‐liquid phase separation–Related genes correlates immune microenvironment profiles and predicts prognosis of lung squamous cell carcinoma

Cited by 6 publications

References 29 publications

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

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

A Liquid–Liquid Phase Separation-Related Index Associate with Biochemical Recurrence and Tumor Immune Environment of Prostate Cancer Patients

Identification of useful genes from multiple microarrays for ulcerative colitis diagnosis based on machine learning methods

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