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Construction of risk score model. (A) Kaplan-Meier survival analysis of bladder cancer patients shows that the high-risk group had significantly worse OS than the low-risk group. (B) Survival rate and survival status of bladder cancer patients. (C) The distribution of 15-lncRNA risk scores for each patient. (D) Heatmap of 15 lncRNAs in the low-risk group and the high-risk group. Cold colors represent low expression and warm colors represent high expression.
Source publication
We investigated whether autophagy-related long noncoding RNAs (lncRNAs) can predict prognosis in bladder cancer. We obtained bladder cancer lncRNA data from The Cancer Genome Atlas and autophagy-related genes from the Human Autophagy Database. Fifteen autophagy-related lncRNAs with prognostic significance were identified. Multivariate Cox analysis...
Context in source publication
Context 1
... set the median risk score as the cutoff value and divided 411 patients into AGING high-risk and low-risk groups. The overall survival (OS) in the low-risk group was significantly better than that in the high-risk group (P <0.001, Figure 4A). Subgroup analysis showed that patients in the high-risk group had worse OS than that in the low-risk group in subgroups based on age, gender, clinical stage, and TNM stage ( Figure 5). ...
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Major fraction of the human genome is transcribed in to the RNA but is not translated in to any specific functional protein. These transcribed but not translated RNA molecules are called as non-coding RNA (ncRNA). There are thousands of different non-coding RNAs present inside the cells, each regulating different cellular pathway/pathways. Over the last few decades non-coding RNAs have been found to be involved in various diseases including cancer. Non-coding RNAs are reported to function both as tumor enhancer and/or tumor suppressor in almost each type of cancer. Urothelial carcinoma of the urinary bladder is the second most common urogenital malignancy in the world. Over the last few decades, non-coding RNAs were demonstrated to be linked with bladder cancer progression by modulating different signalling pathways and cellular processes such as autophagy, metastasis, drug resistance and tumor proliferation. Due to the heterogeneity of bladder cancer cells more in-depth molecular characterization is needed to identify new diagnostic and treatment options. This review emphasizes the current findings on non-coding RNAs and their relationship with various oncological processes such as autophagy, and their applicability to the pathophysiology of bladder cancer. This may offer an understanding of evolving non-coding RNA-targeted diagnostic tools and new therapeutic approaches for bladder cancer management in the future.
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Background
As the main histological subtype of renal cell carcinoma, clear cell renal cell carcinoma (ccRCC) places a heavy burden on health worldwide. Autophagy-related long non-coding RNAs (ARlncRs) have shown tremendous potential as prognostic signatures in several studies, but the relationship between them and ccRCC still has to be demonstrated.
Methods
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Results
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Conclusions
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Background
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Methods
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We successfully constructed and validated an 11 CRs-based model for predicting the prognosis of patients with BLCA. Moreover, we also found 11 CRs-based model was an independent prognostic factor. Functional analysis suggested that CRs were mainly enriched in cancer-related signaling pathways. The CR-based model was also correlated with immune cells infiltration and immune checkpoint. Patients in the high-risk group were more sensitive to several drugs, such as mitomycin C, gemcitabine, cisplatin. Eight small molecule drugs could be beneficial to treatment for BLCA patients.
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Background
Bladder cancer is ranked the second most frequent tumor among urological malignancies. The research strived to establish a prognostic model based on endoplasmic reticulum stress (ERS)-related long non-coding RNA (lncRNA) in bladder cancer.
Methods
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Results
Six ERS-related lncRNAs were identified to be closely coupled with patients’ prognosis. On this foundation, a risk score model was created to generate the risk score for each patient. The ERS-related risk score was shown to be an independent prognostic factor. And the results of GSE31684 dataset also supported this conclusion. Then, a nomogram was constructed based on risk scores and clinical characteristics, and proven to have excellent predictive value. Moreover, the gene function analysis demonstrated that ERS-related lncRNAs were closely linked to fatty extracellular matrix, cytokines, cell adhesion, and tumor pathways. Further analysis revealed the association of the 6-lncRNAs signature with gene mutations and immunity in bladder cancer. Finally, the external datasets and qRT-PCR verified high expressions of the ERS-related lncRNAs in bladder cancer tissues and cells.
Conclusions
Overall, our findings indicated that ERS-related lncRNAs, which may affect tumor pathogenesis in a number of ways, might be exploited to assess the prognosis of bladder cancer patients.
... This may be caused by differences in histological classification. Prognostic lncRNAs in other cancer types have also been reported in recent years, including breast cancer , colon cancer (Zhou et al., 2020), pancreatic cancer (Deng et al., 2020), and bladder cancer (Lai et al., 2020). Most of these models have been constructed based on public databases, with other datasets used for validation. ...
Esophageal squamous cell carcinoma (ESCC), the most prevalent subtype of esophageal cancer, ranks sixth in cancer-related mortality, making it one of the deadliest cancers worldwide. The identification of potential risk factors for ESCC might help in implementing precision therapies. Autophagy-related lncRNAs are a group of non-coding RNAs that perform critical functions in the tumor immune microenvironment and therapeutic response. Therefore, we aimed to establish a risk model composed of autophagy-related lncRNAs that can serve as a potential biomarker for ESCC risk stratification. Using the RNA expression profile from 179 patients in the GSE53622 and GSE53624 datasets, we found 11 lncRNAs (AC004690.2, AC092159.3, AC093627.4, AL078604.2, BDNF-AS, HAND2-AS1, LINC00410, LINC00588, PSMD6-AS2, ZEB1-AS1, and LINC02586) that were co-expressed with autophagy genes and were independent prognostic factors in multivariate Cox regression analysis. The risk model was constructed using these autophagy-related lncRNAs, and the area under the receiver operating characteristic curve (AUC) of the risk model was 0.728. To confirm that the model is reliable, the data of 174 patients from The Cancer Genome Atlas (TCGA) esophageal cancer dataset were analyzed as the testing set. A nomogram for ESCC prognosis was developed using the risk model and clinic-pathological characteristics. Immune function annotation and tumor mutational burden of the two risk groups were analyzed and the high-risk group displayed higher sensitivity in chemotherapy and immunotherapy. Expression of differentially expressed lncRNAs were further validated in human normal esophageal cells and esophageal cancer cells. The constructed lncRNA risk model provides a useful tool for stratifying risk and predicting the prognosis of patients with ESCC, and might provide novel targets for ESCC treatment.
... More and more studies have focused on exploring the association between autophagy and the prognosis of BC (12). However, to the best of our knowledge, none of them are attempting to establish a risk signature for prognosis of BC patient prediction, by using autophagy-related genes (ARGs) (13,14). The aim of the present study was to construct an autophagy-related signature that could accurately act as a prognosis prediction tool in BC. ...
Objective
Bladder cancer (BC) is one of the top ten cancers endangering human health but we still lack accurate tools for BC patients’ risk stratification. This study aimed to develop an autophagy-related signature that could predict the prognosis of BC. In order to provide clinical doctors with a visual tool that could precisely predict the survival probability of BC patients, we also attempted to establish a nomogram based on the risk signature.
Methods
We screened out autophagy-related genes (ARGs) combining weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) in BC. Based on the screened ARGs, we performed survival analysis and Cox regression analysis to identify potential prognostic biomarkers. A risk signature based on the prognostic ARGs by multivariate Cox regression analysis was established, which was validated by using seven datasets. To provide clinical doctors with a useful tool for survival possibility prediction, a nomogram assessed by the ARG-based signature and clinicopathological features was constructed, verified using four independent datasets.
Results
Three prognostic biomarkers including BOC (P = 0.008, HR = 1.104), FGF7(P = 0.030, HR = 1.066), and MAP1A (P = 0.001, HR = 1.173) were identified and validated. An autophagy-related risk signature was established and validated. This signature could act as an independent prognostic feature in patients with BC (P = 0.047, HR = 1.419). We then constructed two nomograms with and without ARG-based signature and subsequent analysis indicated that the nomogram with ARG signature showed high accuracy for overall survival probability prediction of patients with BC (C-index = 0.732, AUC = 0.816). These results proved that the ARG signature improved the clinical net benefit of the standard model based on clinicopathological features (age, pathologic stage).
Conclusions
Three ARGs were identified as prognosis biomarkers in BC. An ARG-based signature was established for the first time, showing strong potential for prognosis prediction in BC. This signature was proven to improve the clinical net benefit of the standard model. A nomogram was established using this signature, which could lead to more effective prognosis prediction for BC patients.
... The recent publication has reported the similar result (HR: 1.03, 95% CI: 1.01-1.05) (22). In 2018, Peng et al. (23) have performed an updated meta-analysis to examine the relationship between metabolic syndrome and susceptibility and prognosis of bladder cancer. ...
Background
Serum gamma-glutamyltransferase (GGT) has been reported to be correlated with survival in a variety of malignancies. However, its effect on patients with bladder cancer (BC) treated by radical cystectomy has never been evaluated.
Patients and Methods
We retrospectively evaluated 263 patients who underwent radical surgery in our center. Baseline features, hematologic variables, and follow-up data were obtained. The endpoints included overall survival (OS), cancer-specific survival (CSS), and disease-free survival (DFS). The relationship between GGT and survival were evaluated.
Results
The median follow-up period for all patients was 34.7 (22.9-45.9) months. At the last follow-up, 67 patients died, 51 patients died of cancer, 92 patients experienced disease recurrence. Patients with an elevated serum GGT had a higher rate of pT3-T4 tumors. Patients with a higher preoperative serum GGT had a lower rate of OS, CSS and DFS (P < 0.001 for all). Multivariate analysis identified that preoperative serum GGT was independent predictor of OS (HR: 3.027, 95% CI: 1.716-5.338; P < 0.001), CSS (HR: 2.115, 95% CI: 1.093-4.090; P = 0.026), DFS (HR: 2.584, 95% CI: 1.569-4.255; P < 0.001). Age, diabetes history, pathologic T stage, and lymph node status also were independent predictors of prognosis for BC patients.
Conclusions
Our results indicated that preoperative serum GGT was an independent prognosis predictor for survival of BC patients after radical cystectomy, and can be included in the prognostic models.
... Emerging evidence has implicated that lncRNAs can affect various autophagy-related genes, and thus regulate cell autophagy in many diseases [17][18][19]. A recent study of bladder cancer has suggested the prognostic potential of autophagy-associated lncRNAs [20]. A latest research about cervical cancer also emphasizes the diagnostic and prognostic value of lncRNAs [21]. ...
Background: Laryngeal squamous cell carcinoma (LSCC) is the second most common malignant tumor in the head and neck. Considering the role of autophagy in tumor development and drug resistance, we investigated the potential prognostic value of autophagy-related long no-coding RNAs (lncRNAs) in LSCC patients.
Methods: Autophagy-related lncRNAs were screened out based on the Cancer Genome Atlas (TCGA) database. Subsequently, five autophagy-related lncRNAs with prognostic value were identified through univariate and multivariate Cox regression analysis. Based on these prognosis-related lncRNAs, a risk signature was established, and the patients with LSCC were divided into the low- and high-risk groups. Finally, a nomogram based on the prognosis signature was constructed.
Results: The overall survival time of the high-risk group was significantly shorter than that of the low-risk one (p<0.0001). Receiver operating characteristic curve (ROC) analysis was performed to further confirm the validity of the model (ROC of training set: 0.841, ROC of test set: 0.718, ROC of entire set: 0.757). Univariate and multivariate regression analyses demonstrated that ours is an independent prognosis signature of LSCC. The nomogram integrating the five-lncRNA signature and clinical factors was then constructed for clinical application.
Conclusion: In summary, our signature of the five autophagy-associated lncRNAs can serve as an effective prognostic indicator for patients with LSCC.
