A Novel Transcription Factor-Based Prognostic Signature in Endometrial Cancer: Establishment and Validation

Yang, Xiao; Cheng, Yuan; Li, Xingchen; Zhou, Jingyi; Dong, Yangyang; Shen, Boqiang; Zhao, Lijun; Wang, Jianliu

doi:10.2147/ott.s293085

Cited by 9 publications

(7 citation statements)

References 49 publications

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“…A 9-TF signature can be predictive of breast cancer recurrence for optimizing clinical management [ 29 ]. Additionally, Yang et al proposed a TF-based prognostic signature that reliably predicts endometrial cancer individuals' survival outcomes [ 30 ]. Here, through univariate analyses followed by stepwise multivariate Cox regression analyses, we developed a TF genomic model for lung cancer outcomes.…”

Section: Discussionmentioning

confidence: 99%

Integrative Molecular Analyses of an Individual Transcription Factor-Based Genomic Model for Lung Cancer Prognosis

et al. 2021

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Objective. Precision medicine with molecular profiles has revolutionized the management of lung cancer contributing to improved prognosis. Herein, we aimed to uncover the gene expression profiling of transcription factors (TFs) in lung cancer as well as to develop a TF-based genomic model. Methods. We retrospectively curated lung cancer patients from public databases. Through comparing mRNA expression profiling between lung cancer and normal specimens, specific TFs were determined. Thereafter, a TF genomic model was developed with univariate Cox regression and stepwise multivariable Cox analyses, which was verified through the GSE72094 dataset. Gene set enrichment analyses (GSEA) were presented. Downstream targets of TFs were predicted with ChEA, JASPAR, and MotifMap projects, and their biological significance was investigated through the clusterProfiler algorithm. Results. In the TCGA cohort, we proposed a TF-based genomic model, comprised of SATB2, HLF, and NPAS2. Lung cancer individuals were remarkably stratified into high- and low-risk groups. Survival analyses uncovered that high-risk populations presented unfavorable survival outcomes. ROCs confirmed the excellent predictive potency in patients’ prognosis. Additionally, this model was an independent prognostic indicator in accordance with multivariate analyses. The clinical implication of the model was well verified in an independent dataset. High risk score was in relation to carcinogenic pathways. Downstream targets were characterized by immune and carcinogenic activation. Conclusion. The proposed TF genomic model acts as a promising marker for estimation of lung cancer patients’ outcomes. Prospective research is required for testing the clinical utility of the model in individualized management of lung cancer.

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Section: Discussionmentioning

confidence: 99%

Integrative Molecular Analyses of an Individual Transcription Factor-Based Genomic Model for Lung Cancer Prognosis

et al. 2021

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“…However, no information on other clinical predictors was available in this study and the training and validation cohort had the same origin with a risk of overfitting. Other developed signatures were often developed using data from TCGA patients without information on the surgical techniques of the center, fixation's delays and clinical data [46,48]. Those parameters (in particular the time of ischemia before the formol fixation) are critical and could dramatically affect the gene expression in endometrial cancer.…”

Section: Discussionmentioning

confidence: 99%

Biomarkers Associated with Lymph Nodal Metastasis in Endometrioid Endometrial Carcinoma

Mairé

Bourdon

Soubeyran

et al. 2022

Cancers

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Introduction. Lymph node metastasis is determinant in the prognosis and treatment of endometrioid endometrial cancer (EEC) but the risk–benefit balance of surgical lymph node staging remains controversial. Objective. Describe the pathways associated with lymph node metastases in EEC detected by whole RNA sequencing. Methods. RNA-sequencing was performed on a retrospective series of 30 non-metastatic EEC. N+ and N− patients were matched for tumoral size, tumoral grade and myometrial invasion. Results. Twenty-eight EECs were analyzable (16 N+ and 12 N−). Bioinformatics Unsupervised analysis revealed three patterns of expression, enriched in N+, mix of N+/N− and enriched in N−, respectively. The cluster with only N+ patient overexpressed extra cellular matrix, epithelial to mesenchymal and smooth muscle contraction pathways with respect to the N− profile. Differential expression analysis between N+ and N− was used to generate a 54-genes signature with an 87% accuracy. Conclusion. RNA-expression analysis provides a basis to develop a gene expression-based signature that could pre-operatively predict lymph node invasion.

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“…As for PCa, a TF named HOXB13 has been found to regulate the lncRNA HOXA11-AS for promoting bone metastasis in PCa, by CCL2/CCR2 cytokine regulation and integrin signaling in autocrine and paracrine processes (76). In recent years, a TF-based regulatory network has been established to clarify the underlying regulation circuits and mechanisms (77)(78)(79)(80). Ning et al (81) constructed a feedforward network mediated by lncRNA and illustrated that many TF-lncRNA interactions were implicated in prognostic motifs.…”

Section: Discussionmentioning

confidence: 99%

Mutator-Derived lncRNA Landscape: A Novel Insight Into the Genomic Instability of Prostate Cancer

Tang

et al. 2022

Front. Oncol.

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BackgroundIncreasing evidence has emerged to reveal the correlation between genomic instability and long non-coding RNAs (lncRNAs). The genomic instability-derived lncRNA landscape of prostate cancer (PCa) and its critical clinical implications remain to be understood.MethodsPatients diagnosed with PCa were recruited from The Cancer Genome Atlas (TCGA) program. Genomic instability-associated lncRNAs were identified by a mutator hypothesis-originated calculative approach. A signature (GILncSig) was derived from genomic instability-associated lncRNAs to classify PCa patients into high-risk and low-risk groups. The biochemical recurrence (BCR) model of a genomic instability-derived lncRNA signature (GILncSig) was established by Cox regression and stratified analysis in the train set. Then its prognostic value and association with clinical features were verified by Kaplan–Meier (K-M) analysis and receiver operating characteristic (ROC) curve in the test set and the total patient set. The regulatory network of transcription factors (TFs) and lncRNAs was established to evaluate TF–lncRNA interactions.ResultsA total of 95 genomic instability-associated lncRNAs of PCa were identified. We constructed the GILncSig based on 10 lncRNAs with independent prognostic value. GILncSig separated patients into the high-risk (n = 121) group and the low-risk (n = 121) group in the train set. Patients with high GILncSig score suffered from more frequent BCR than those with low GILncSig score. The results were further validated in the test set, the whole TCGA cohort, and different subgroups stratified by age and Gleason score (GS). A high GILncSig risk score was significantly associated with a high mutation burden and a low critical gene expression (PTEN and CDK12) in PCa. The predictive performance of our BCR model based on GILncSig outperformed other existing BCR models of PCa based on lncRNAs. The GILncSig also showed a remarkable ability to predict BCR in the subgroup of patients with TP53 mutation or wild type. Transcription factors, such as FOXA1, JUND, and SRF, were found to participate in the regulation of lncRNAs with prognostic value.ConclusionIn summary, we developed a prognostic signature of BCR based on genomic instability-associated lncRNAs for PCa, which may provide new insights into the epigenetic mechanism of BCR.

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A Novel Transcription Factor-Based Prognostic Signature in Endometrial Cancer: Establishment and Validation

Cited by 9 publications

References 49 publications

Integrative Molecular Analyses of an Individual Transcription Factor-Based Genomic Model for Lung Cancer Prognosis

Integrative Molecular Analyses of an Individual Transcription Factor-Based Genomic Model for Lung Cancer Prognosis

Biomarkers Associated with Lymph Nodal Metastasis in Endometrioid Endometrial Carcinoma

Mutator-Derived lncRNA Landscape: A Novel Insight Into the Genomic Instability of Prostate Cancer

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