Using mRNAsi to identify prognostic-related genes in endometrial carcinoma based on WGCNA

Liu, Jinhui; Wu, Zhipeng; Sun, Rui; Nie, Song; Meng, Hua; Zhong, Yi; Nie, Xianglin; Cheng, Wenjun

doi:10.1016/j.lfs.2020.118231

Cited by 23 publications

(17 citation statements)

References 42 publications

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“…In the testing cohort, the results of Kaplan–Meier survival analysis showed that high-risk patients were associated with poor prognosis ( P =6.727 e − 03; Figure 5(c) ), and the result of ROC curve showed that the AUC values of the risk signature for predicting 1-, 3-, and 5-year survival were 0.629, 0.638, and 0.687 ( Figure 5(c) ), respectively. In the entire cohort, we validated the risk signature and compared it with another stemness-associated risk signature constructed by Liu et al [ 15 ]. Patients in the high-risk group divided by our risk signature had a worse prognosis than did those in the low-risk group ( P =3.719 e − 10; Figure 5(d) ), and this difference was more significant than that of Liu's risk signature ( P =2.025 e − 07).…”

Section: Resultsmentioning

confidence: 99%

“…In both cohorts, the number of deaths in the high-risk group was greater than that in the Figure 5(c)), respectively. In the entire cohort, we validated the risk signature and compared it with another stemness-associated risk signature constructed by Liu et al [15]. Patients in the high-risk group divided by our risk signature had a worse prognosis than did those in the low-risk group (P � 3.719e − 10; Figure 5(d)), and this difference was more significant than that of Liu's risk signature (P � 2.025e − 07).…”

Section: Verification and Comparation Of The Risk Signaturementioning

confidence: 99%

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A Risk Signature with Nine Stemness Index-Associated Genes for Predicting Survival of Patients with Uterine Corpus Endometrial Carcinoma

Zou

Liu

et al. 2021

Journal of Oncology

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Purpose. To identify mRNA expression-based stemness index- (mRNAsi-) related genes and build an mRNAsi-related risk signature for endometrial cancer. Methods. We collected mRNAsi data of endometrial cancer samples from The Cancer Genome Atlas (TCGA) and analyzed their relationship with the main clinicopathological characteristics and prognosis of endometrial cancer patients. We screened the top 50% of the genes in TCGA for weighted gene correlation network analysis (WGCNA) to explore mRNAsi-related gene sets. Among these mRNAsi-related genes, we further screened for those related to the prognosis of endometrial cancer patients via univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis. Using stepwise multivariate Cox regression analysis, a stemness index-related risk signature was constructed. Finally, we identified potential prognostic biomarkers for endometrial cancer by combining the GEO database and immunohistochemical staining. Results. The mRNAsi of endometrial cancer samples was significantly higher than that of normal samples and was related to the International Federation of Gynecology and Obstetrics (FIGO) stage, pathological grade, postoperative tumor status, and overall survival of endometrial cancer patients. We identified 21 mRNAsi-related gene modules, and 1,324 genes were obtained from the most relevant module. TCGA samples were divided into training and validation cohorts, and the training cohort was used to construct a nine-mRNAsi-related gene signature (B3GAT2, CD3EAP, DMC1, FRMPD3, LINC01224, LINC02068, LY6H, NR6A1, and TLE2). High-risk and low-risk patients had significant prognostic differences, and the risk signature could accurately predict their 1-, 3-, and 5-year survival. The nomogram composed of risk score and multiple clinicopathological features could accurately predict 1-, 3-, and 5-year survival. Finally, CD3EAP was found to be a novel prognostic biomarker for endometrial cancer. Conclusion. Endometrial cancer cell stemness is related to patient prognosis. The nine-gene risk signature is an independent prognostic factor and can accurately predict endometrial cancer patient prognosis.

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Section: Resultsmentioning

confidence: 99%

Section: Verification and Comparation Of The Risk Signaturementioning

confidence: 99%

A Risk Signature with Nine Stemness Index-Associated Genes for Predicting Survival of Patients with Uterine Corpus Endometrial Carcinoma

Zou

Liu

et al. 2021

Journal of Oncology

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“…In 2018, Malta et al (2018) proposed the conception of mRNAsi, which is considered as a more comprehensive index to uncover the characteristics of CSCs. Subsequently, mRNAsi was widely applied to reveal the stemness-related characteristics of different cancers, such as lung cancer (Qin et al, 2020;Zhang et al, 2020a), bladder cancer (Pan et al, 2019), endometrial carcinoma (Liu et al, 2020), medulloblastoma (Lian et al, 2019), and breast cancer (Pei et al, 2020). Moreover, mRNAsi was also used to identify the prognostic biomarkers and therapeutic targets associated with CSC characteristics of glioma (Lvu et al, 2020;Xia et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

“…Recently, the mRNA expressionbased stemness index (mRNAsi), which represents the transcriptomic stemness expression, has been applied to assess CSC characteristics (Malta et al, 2018). In some cancers, such as bladder, lung, breast, or endometrial carcinoma, it has been reported that mRNAsi is a credible marker and is associated with tumor stage (Pan et al, 2019;Liu et al, 2020;Pei et al, 2020). However, its roles in glioma are rare known.…”

Section: Introductionmentioning

confidence: 99%

Molecular Subtypes Based on the Stemness Index Predict Prognosis in Glioma Patients

Tan

Zhu²,

Tang

et al. 2021

Front. Genet.

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Glioma is the common histological subtype of malignancy in the central nervous system, with high morbidity and mortality. Glioma cancer stem cells (CSCs) play essential roles in tumor recurrence and treatment resistance. Thus, exploring the stem cell-related genes and subtypes in glioma is important. In this study, we collected the RNA-sequencing (RNA-seq) data and clinical information of glioma patients from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. With the differentially expressed genes (DEGs) and weighted gene correlation network analysis (WGCNA), we identified 86 mRNA expression-based stemness index (mRNAsi)-related genes in 583 samples from TCGA RNA-seq dataset. Furthermore, these samples from TCGA database could be divided into two significantly different subtypes with different prognoses based on the mRNAsi corresponding gene, which could also be validated in the CGGA database. The clinical characteristics and immune cell infiltrate distribution of the two stemness subtypes are different. Then, functional enrichment analyses were performed to identify the different gene ontology (GO) terms and pathways in the two different subtypes. Moreover, we constructed a stemness subtype-related risk score model and nomogram to predict the prognosis of glioma patients. Finally, we selected one gene (ETV2) from the risk score model for experimental validation. The results showed that ETV2 can contribute to the invasion, migration, and epithelial-mesenchymal transition (EMT) process of glioma. In conclusion, we identified two distinct molecular subtypes and potential therapeutic targets of glioma, which could provide new insights for the development of precision diagnosis and prognostic prediction for glioma patients.

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“…2.9. WGCNA analysis First, as precviously described, differentially expressed genes (DEGs) between EC and normal specimens in the TCGA dataset were screened out using the "limma" package with threshold of adjusted P-value < 0.05 and |log2FC| >1 [21]. Next the expression pro le of these DEGs was sent to establish a gene coexpression network by the weighted gene co-expression network analysis (WGCNA) package in R language as precviously described [22].…”

Section: Methylation and Mutantion Analysismentioning

confidence: 99%

Low Expression of RILPL2 Predicts Poor Prognosis and Correlates with Immune Infiltration in Endometrial Carcinoma

Liu

Mengtimg

et al. 2021

Preprint

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Increasing numbers of biomarkers have been identified for various cancers. However, biomarkers associated with endometrial carcinoma (EC) remain largely to be explored. In the current research, we downloaded the RNA-seq data and corresponding clinicopathological features from the Cancer Genome Atlas (TCGA) database. We conducted expression analysis, which resulted in identification of RILPL2 as a novel diagnostic biomarker in EC. The dysregulation of RILPL2 in EC was also validated in multiple datasets. The correlations between clinical features and RILPL2 expression were assessed by logistic regression analysis. Then, Kaplan-Meier analysis, univariate, and multivariate Cox regression analysis were performed to estimate prgnostic values of RILPL2 in the TCGA cohort, which unveiled that increased level of RILPL2 was remarkably associated with better prognosis and could be severd as an independent prognostic biomarker in patients with EC. Moreover, correlation analysis of RILPL2 and tumor-infiltrating immune cells (TIICs) indicated that RILPL2 might play a critical role in regulating immune cell infiltration in EC and is related to immune response. Besides, high methylation level was a significant cause for RILPL2 low expression in EC. Subsequently, weighted gene co-expression network analysis (WGCNA) and enrichment analysis were conducted to explore the RILPL2-involved underlyingl oncogenic mechanisms, and the results indicated that RILPL2 mainly regulated cell cycle. In conclusion, our findings provided evidence that downregulation of RILPL2 in EC is an indicator of adverse prognosis and RILPL2 may act as a promising target for the theraputics of EC.

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Using mRNAsi to identify prognostic-related genes in endometrial carcinoma based on WGCNA

Cited by 23 publications

References 42 publications

A Risk Signature with Nine Stemness Index-Associated Genes for Predicting Survival of Patients with Uterine Corpus Endometrial Carcinoma

A Risk Signature with Nine Stemness Index-Associated Genes for Predicting Survival of Patients with Uterine Corpus Endometrial Carcinoma

Molecular Subtypes Based on the Stemness Index Predict Prognosis in Glioma Patients

Low Expression of RILPL2 Predicts Poor Prognosis and Correlates with Immune Infiltration in Endometrial Carcinoma

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