Prognostic value of an autophagy-related long-noncoding-RNA signature for endometrial cancer

Wang, Xiufang; Dai, Chenyang; Ye, Minqing; Wang, Jingyun; Lin, Weizhao; Li, Ruiman

doi:10.18632/aging.202431

Cited by 28 publications

(21 citation statements)

References 42 publications

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“…In this genomic era, a large number of genome-sequencing technologies and data have emerged, 16 and researchers began to focus on the prognosis prediction of UCEC using The Cancer Genome Atlas (TCGA)-UCEC cohort. 17 , 18 , 19 , 20 Wang et al. 17 constructed an autophagy-related long-noncoding RNA signature to predict the prognosis of UCEC.…”

Section: Introductionmentioning

confidence: 99%

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Development of a seven-gene tumor immune microenvironment prognostic signature for high-risk grade III endometrial cancer

Zheng

Gou

et al. 2021

Molecular Therapy - Oncolytics

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Uterine corpus endometrial carcinoma locally infiltrates numerous immune cells and other tumor immune microenvironment components. These cells are involved in malignant tumor growth and proliferation and the process of resistance toward immunotherapies. Here, we aimed to develop a tumor immune microenvironment-related prognostic signature for high-risk grade III endometrial carcinoma based on The Cancer Genome Atlas. The signature was systematically correlated with immune infiltration characteristics of the tumor microenvironment. The seven-gene Riskscore signature was robust and performed well in training, testing, and Gene Expression Omnibus-independent cohorts. A nomogram comprising the gene signature accurately predicted patient prognosis, with our model performing better than other endometrial cancer-related signatures. Analysis of the IMvigor210 immunotherapy cohort revealed that subgroups with a low Riskscore had a better prognosis than subgroups with a high Riskscore. Subgroups with a low Riskscore exhibited immune cell infiltration and inflammatory profiles, whereas subgroups with a high Riskscore experienced progressive disease. The receiver operating characteristic curve indicated that risk score, neoantigen, and tumor mutation burden models together accurately predicted treatment response. Taken together, we developed a tumor microenvironment-based seven-gene prognostic stratification system to predict the prognosis of patients with high-risk endometrial cancer and guide more effective immunotherapy strategies.

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Section: Introductionmentioning

confidence: 99%

“… 17 , 18 , 19 , 20 Wang et al. 17 constructed an autophagy-related long-noncoding RNA signature to predict the prognosis of UCEC. Jiang et al.…”

Section: Introductionmentioning

confidence: 99%

Development of a seven-gene tumor immune microenvironment prognostic signature for high-risk grade III endometrial cancer

Zheng

Gou

et al. 2021

Molecular Therapy - Oncolytics

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“…Recent studies have presented signatures for cancer diagnosis and survival prediction based on the specific expression levels of coding genes or noncoding RNAs ( Liu et al, 2021 ; Wang R. et al, 2021 ; Wang X. et al, 2021 ; Wu et al, 2021 ), which required certain testing methods. In this study, irlncRNA pairs were constructed and used for the development of prognosis signature.…”

Section: Discussionmentioning

confidence: 99%

Using Immune-Related Long Non-coding Ribonucleic Acids to Develop a Novel Prognosis Signature and Predict the Immune Landscape of Colon Cancer

Wang

Chen

Wang

et al. 2021

Front. Cell Dev. Biol.

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Purpose: This study aimed to construct a novel signature to predict the survival of patients with colon cancer and the associated immune landscape, based on immune-related long noncoding ribonucleic acids (irlncRNAs).Methods: Expression profiles of irlncRNAs in 457 patients with colon cancer were retrieved from the TCGA database (https://portal.gdc.cancer.gov). Differentially expressed (DE) irlncRNAs were identified and irlncRNA pairs were recognized using Lasso regression and Cox regression analyses. Akaike information criterion (AIC) values of receiver operating characteristic (ROC) curve were calculated to identify the ideal cut-off point for dividing patients into two groups and constructing the prognosis signature. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to validate the expression of LINC02195 and SCARNA9 in colon cancer.Results: We identified 22 irlncRNA pairs and patients were divided into high-risk and low-risk groups based on the calculated risk score using these 22 irlncRNA pairs. The irlncRNA pairs were significantly related to patient survival. Low-risk patients had a significantly longer survival time than high-risk patients (p < 0.001). The area under the curve of the signature to predict 5-year survival was 0.951. The risk score correlated with tumor stage, infiltration depth, lymph node metastasis, and distant metastasis. The risk score remained significant after univariate and multivariate Cox regression analyses. A nomogram model to predict patient survival was developed based on the results of Cox regression analysis. Immune cell infiltration status, expression of some immune checkpoint genes, and sensitivity to chemotherapeutics were also related to the risk score. The results of qRT-PCR revealed that LINC02195 and SCARNA9 were significantly upregulated in colon cancer tissues.Conclusion: The constructed prognosis signature showed remarkable efficiency in predicting patient survival, immune cell infiltration status, expression of immune checkpoint genes, and sensitivity to chemotherapeutics.

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“…The association of lncRNAs with ferroptosis-related genes was evaluated by Pearson correlation analysis. A Pearson correlation coefficient (PCC) greater than 0.3 and a P-value smaller than 0.001 were used to identify ferroptosis-related lncRNAs (26)(27)(28). Univariate Cox regression method was performed to analyze the prognostic role of the ferroptosisrelated lncRNAs.…”

Section: Identification Of the Ferroptosis-related Lncrna Prognostic Signaturementioning

confidence: 99%

A ferroptosis-related lncRNA signature predicts prognosis in ovarian cancer patients

Peng¹,

Hu²,

Rao³

et al. 2021

Transl Cancer Res TCR

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Background: Ovarian cancer (OC) is a common gynecological malignant tumor with poor prognosis.Ferroptosis is an iron-dependent modality of regulated cell death. The purpose of this study was to determine the prognostic ability of ferroptosis-related long non-coding RNAs (lncRNAs) in OC patients and construct a ferroptosis-related lncRNA prognostic model. Methods: The Cancer Genome Atlas (TCGA) and FerrDb databases were used to collect RNA sequencing data of OC patients and ferroptosis-related genes, respectively. OC patients were randomly assigned to the training or testing set. Pearson correlation analysis was used to identify ferroptosis-related lncRNAs. Univariate Cox, Least Absolute Shrinkage and Selection Operator (LASSO), and multivariate regression analyses were performed in the training set to develop a predictive model. The model was validated in the testing set and entire set. Survival analysis, receiver operating characteristic curves, independent prognostic factor analysis, and correlation analysis with clinical features were performed to evaluate the predictive value of the model. A nomogram was established to predict the survivability of OC patients over 1, 3, and 5 years. The distribution of distinct groups was investigated using principal component analysis, and the underlying the biological functions were explored using gene set enrichment analysis. Results: Eleven ferroptosis-related lncRNAs were determined to establish the prognostic model. Patients in the high-risk group had poor prognosis compared with the low-risk group in the training, testing and entire sets. The area under the receiver operating characteristic curve corresponding to 1-, 3-, and 5-year survival were 0.731, 0.796, and 0.805 in the training set; 0.704, 0.597, and 0.655 in the testing set; and 0.715, 0.691, and 0.736, in the entire set, respectively. The risk score correlated with age and grade. The risk score was also an independent prognostic factor in OC. A nomogram with high C-index (0.68) was constructed. An intuitive observation of the principal component analysis revealed a distinction between high-and lowrisk groups, and gene set enrichment analysis indicated that cancer-related pathways were enriched in the high-risk group. Conclusions: The signature composed of 11 ferroptosis-related lncRNAs accurately predicted the prognosis of OC patients.

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Prognostic value of an autophagy-related long-noncoding-RNA signature for endometrial cancer

Cited by 28 publications

References 42 publications

Development of a seven-gene tumor immune microenvironment prognostic signature for high-risk grade III endometrial cancer

Development of a seven-gene tumor immune microenvironment prognostic signature for high-risk grade III endometrial cancer

Using Immune-Related Long Non-coding Ribonucleic Acids to Develop a Novel Prognosis Signature and Predict the Immune Landscape of Colon Cancer

A ferroptosis-related lncRNA signature predicts prognosis in ovarian cancer patients

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