&lt;p&gt;Decreased PTGDS Expression Predicting Poor Survival of Endometrial Cancer by Integrating Weighted Gene Co-Expression Network Analysis and Immunohistochemical Validation&lt;/p&gt;

Zou, Ruoyao; Zheng, Mingjun; Tan, Mingzi; Xu, Haoya; Luan, Nannan; Zhu, Liping

doi:10.2147/cmar.s255753

Cited by 14 publications

(17 citation statements)

References 43 publications

(43 reference statements)

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“…Observed from the left part of the diagram, there is more percentage of patients with BRCA, UCEC or SKCM mapped to favorable lncRNA phenotypes compared to patients with HNSC, which represents patients with the first three cancer types tended to have better overall prognosis than patients with HNSC. This result is consistent with the literature [37][38][39], i.e. BRCA, UCEC, and SKCM patients have a much higher five-year survival rate (>90%, 90%, and 92% respectively) compared to HNSC (50%).…”

Section: B Mapping Of 5-lncrna Signature To Patient Prognosissupporting

confidence: 93%

Identification of lncRNA Signature Associated With Pan-Cancer Prognosis

Bao

Wang

et al. 2021

IEEE J. Biomed. Health Inform.

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Long noncoding RNAs (lncRNAs) have emerged as potential prognostic markers in various human cancers as they participate in many malignant behaviors. However, the value of lncRNAs as prognostic markers among diverse human cancers is still under investigation, and a systematic signature based on these transcripts that related to pan-cancer prognosis has yet to be reported. In this study, we proposed a framework to incorporate statistical power, biological rationale, and machine learning models for pan-cancer prognosis analysis. The framework identified a 5-lncRNA signature (ENSG00000206567, PCAT29, ENSG00000257989, LOC388282, and LINC00339) from TCGA training studies (n=1,878). The identified lncRNAs are significantly associated (all P ≤1.48E-11) with overall survival (OS) of the TCGA cohort (n=4,231). The signature stratified the cohort into low-and high-risk groups with significantly distinct survival outcomes (median OS of 9.84 years versus 4.37 years, log-rank P=1.48E-38) and achieved a time-dependent ROC/AUC of 0.66 at 5 years. After routine clinical factors involved, the signature demonstrated better performance for long-term prognostic estimation (AUC of 0.72). Moreover, the signature was further evaluated on two independent external cohorts (TARGET, n=1,122; CPTAC, n=391; National Cancer Institute) which yielded similar prognostic values (AUC of 0.60 and 0.75; logrank P=8.6E-09 and P=2.7E-06). An indexing system was developed to map the 5-lncRNA signature to prognoses of pan-cancer patients. In silico functional analysis indicated that the lncRNAs are associated with common biological processes driving human cancers. The five lncRNAs, especially ENSG00000206567, ENSG00000257989 and LOC388282 that never reported before, may serve as viable molecular targets common among diverse cancers.

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Section: B Mapping Of 5-lncrna Signature To Patient Prognosissupporting

confidence: 93%

Identification of lncRNA Signature Associated With Pan-Cancer Prognosis

Bao

Wang

et al. 2021

IEEE J. Biomed. Health Inform.

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“…Previous studies have also found gene prognostic markers closely related to UCEC (68)(69)(70), but it is the first time to construct a model in Treg cells to predict the prognosis of UCEC. AUC is a crucial standard to judge whether a prediction model has good discrimination.…”

Section: Discussionmentioning

confidence: 99%

Development and Clinical Validation of Novel 8-Gene Prognostic Signature Associated With the Proportion of Regulatory T Cells by Weighted Gene Co-Expression Network Analysis in Uterine Corpus Endometrial Carcinoma

et al. 2021

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BackgroundUterine corpus endometrial carcinoma (UCEC) is a gynecological malignant tumor with low survival rate and poor prognosis. The traditional clinicopathological staging is insufficient to estimate the prognosis of UCEC. It is necessary to select a more effective prognostic signature of UCEC to predict the prognosis and immunotherapy effect of UCEC.MethodsCIBERSORT and weighted correlation network analysis (WGCNA) algorithms were combined to screen modules related to regulatory T (Treg) cells. Subsequently, univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were used to identify the genes in key modules. The difference in overall survival (OS) between high- and low-risk patients was analyzed by Kaplan–Meier analysis. The Tregs-related risk signature (TRRS) was screened by uni- and multivariate Cox analyses. Afterward, we analyzed the expression difference of TRRS and verified its ability to predict the prognosis of UCEC and the effect of immunotherapy.ResultsRed module has the highest correlation with Tregs among all clustered modules. Pathways enrichment indicated that the related processes of UCEC were primarily associated to the immune system. Eight genes (ZSWIM1, NPRL3, GOLGA7, ST6GALNAC4, CDC16, ITPK1, PCSK4, and CORO1B) were selected to construct TRRS. We found that this TRRS is a significantly independent prognostic factor of UCEC. Low-risk patients have higher overall survival than high-risk patients. The immune status of different groups was different, and tumor-related pathways were enriched in patients with higher risk score. Low-risk patients are more likely take higher tumor mutation burden (TMB). Meanwhile, they are more sensitive to chemotherapy than patients with high-risk score, which indicated a superior prognosis. Immune checkpoints such as PD-1, CTLA4, PD-L1, and PD-L2 all had a higher expression level in low-risk group. TRRS expression really has a relevance with the sensitivity of UCEC patients to chemotherapeutic drugs.ConclusionWe developed and validated a TRRS to estimate the prognosis and reflect the immune status of UCEC, which could accurately assess the prognosis of patients with UCEC and supply personalized treatments for them.

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“…Recently, WGCNA analysis is widely used to construct the modules of co-expression genes that relate to prognosis or other clinical outcomes. For instance, researchers found that Prostaglandin D2 Synthase (PTGDS) predicted poor survival, while ANO1 might be a potential marker for good prognosis in endometrial cancer by WGCNA ( Wang et al, 2019 ; Zou et al, 2020 ). Besides, an increasing number of research apply the machine learning into the biomedical field.…”

Section: Discussionmentioning

confidence: 99%

Identification of a MicroRNA Signature Associated With Lymph Node Metastasis in Endometrial Endometrioid Cancer

Song

et al. 2021

Front. Genet.

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BackgroundLymph node metastasis (LNM) is an important prognostic factor in endometrial cancer. Anomalous microRNAs (miRNAs) are associated with cell functions and are becoming a powerful tool to characterize malignant transformation and metastasis. The aim of this study was to construct a miRNA signature to predict LNM in endometrial endometrioid carcinoma (EEC).MethodCandidate target miRNAs related to LNM in EEC were screened by three methods including differentially expressed miRNAs (DEmiRs), weighted gene co-expression network analysis (WGCNA), and decision tree algorithms. Samples were randomly divided into the training and validation cohorts. A miRNA signature was built using a logistic regression model and was evaluated by the area under the curve (AUC) of receiver operating characteristic curve (ROC) and decision curve analysis (DCA). We also conducted pathway enrichment analysis and miRNA–gene regulatory network to look for potential genes and pathways engaged in LNM progression. Survival analysis was performed, and the miRNAs were tested whether they expressed differently in another independent GEO database.ResultThirty-one candidate miRNAs were screened and a final 15-miRNA signature was constructed by logistic regression. The model showed good calibration in the training and validation cohorts, with AUC of 0.824 (95% CI, 0.739–0.912) and 0.821 (95% CI, 0.691–0.925), respectively. The DCA demonstrated the miRNA signature was clinically useful. Hub miRNAs in signature seemed to contribute to EEC progression via mitotic cell cycle, cellular protein modification process, and molecular function. MiR-34c was statistically significant in survival that a higher expression of miR-34c indicated a higher survival time. MiR-34c-3p, miR-34c-5p, and miR-34b-5p were expressed differentially in GSE75968.ConclusionThe miRNA signature could work as a noninvasive method to detect LNM in EEC with a high prediction accuracy. In addition, miR-34c cluster may be a key biomarker referring LNM in endometrial cancer.

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<p>Decreased PTGDS Expression Predicting Poor Survival of Endometrial Cancer by Integrating Weighted Gene Co-Expression Network Analysis and Immunohistochemical Validation</p>

Cited by 14 publications

References 43 publications

Identification of lncRNA Signature Associated With Pan-Cancer Prognosis

Identification of lncRNA Signature Associated With Pan-Cancer Prognosis

Development and Clinical Validation of Novel 8-Gene Prognostic Signature Associated With the Proportion of Regulatory T Cells by Weighted Gene Co-Expression Network Analysis in Uterine Corpus Endometrial Carcinoma

Identification of a MicroRNA Signature Associated With Lymph Node Metastasis in Endometrial Endometrioid Cancer

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