Dataset Information

Construction of an individualized clinical prognostic index based on ubiquitination-associated lncRNA in clear cell renal cell carcinoma patients.

ABSTRACT:

Background

ccRCC is considered as the main subtype of RCC, which accounted for sixth deadliest cancer worldwide. Recently, ubiquitination has been reported to be closely involved in the progression of tumore. The purpose of this study was to identify the ubiquitination-associated genes and co-expressed lncRNAs on the prognosis of clear cell renal cell carcinoma (ccRCC) patients.

Methods and patients

We downloaded 530 cases and the corresponding transcriptome profiling from The Cancer Genome Atlas (TCGA) database. We distinguished mRNA and lncRNA expression data from the transcriptome profiling and then extracted the expression of mRNAs that regulate protein ubiquitination. We obtained lncRNAs associated with protein ubiquitination regulation from the lncRNA data by gene co-expression analysis. Cox regression analysis of survival time, survival status, and lncRNA expression level was carried out, and a prognostic index (PI) was constructed.

Results

The PI was established based on 8 prognostic lncRNAs that regulate protein ubiquitination and distinguish the high-risk group patients from all patients. Multivariate analysis indicated that this PI was an individualized clinical prognostic factor for patients with ccRCC. Regarding clinical characteristics, a ubiquitination-associated clinical-prognostic index (UCPI), containing 8 ubiquitination-related lncRNAs and age, was established and tested with AUC of 0.80.

Conclusion

We established a UCPI containing 8 lncRNAs related to protein ubiquitination. This UCPI may become an appropriate model to predict the prognosis in ccRCC patients and guide clinicians to adjust the follow-up regimen.

SUBMITTER: Liu K

PROVIDER: S-EPMC9087998 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Publications

Construction of an individualized clinical prognostic index based on ubiquitination-associated lncRNA in clear cell renal cell carcinoma patients.

Liu Kun K Liu Xuzhong X Sun Qing Q Tang Zhiwang Z Wang Gongcheng G Xu Zongyuan Z

World journal of surgical oncology 20220510 1

<h4>Background</h4>ccRCC is considered as the main subtype of RCC, which accounted for sixth deadliest cancer worldwide. Recently, ubiquitination has been reported to be closely involved in the progression of tumore. The purpose of this study was to identify the ubiquitination-associated genes and co-expressed lncRNAs on the prognosis of clear cell renal cell carcinoma (ccRCC) patients.<h4>Methods and patients</h4>We downloaded 530 cases and the corresponding transcriptome profiling from The Can ...[more]

PMID: 35538487

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Project description:BackgroundKidney renal clear cell carcinoma (KIRC) lacks effective prognostic biomarkers and the role and mechanism of N6-methyladenosine (m6A) modification of long noncoding RNAs (lncRNAs) in KIRC remain unclear.MethodsWe extracted standard mRNA-sequencing and clinical data from the TCGA database. The prognostic risk model was obtained by Lasso regression and Cox regression. We randomly divided the samples into training and test sets, each taking half of the cases. Based on Lasso regression and Cox regression for training set, the prognostic risk signature was constructed; risk scores were calculated with the R package "glmnet." Based on the median value of the prognostic risk score, risk scores were calculated for each patient and we divided all KIRC samples into high-risk and low-risk groups. Then, high- and low-risk subtypes were established and their prognosis, clinical features, and immune infiltration microenvironment were evaluated in test set and the entire sampled data set. The reliability of the prognostic model was confirmed by receiver operating characteristic curve analysis.ResultsWe found 28 prognostic m6A-related lncRNAs and established a m6A-related lncRNAs prognostic signature. Risk score=AC015813.1∗(0.0086)+EMX2OS∗(-0.0101)+LINC00173∗(0.0309)+PWAR5∗(-0.0146)+SNHG1∗(0.0043). The signature showed a better predictive ability than other clinical indicators, including tumor node metastasis classification (TNM), histological, and pathological stages. In the high-risk group, M0 macrophages, CD8+ T cells, and regulatory T cells had significantly higher scores. Contrarily, in the low-risk group, activated dendritic cells, M1 macrophages, mast resting cells, and monocytes had significantly higher scores. In the high-risk group, LSECtin was overexpressed. In the low-risk group, PD-L1 was overexpressed. Moreover, high-risk patients may benefit more from AZ628.ConclusionsIn conclusion, prognosis prediction of patients with KIRC and new insights for immunotherapy are provided by the m6A-related lncRNA prognostic signature.

Project description:Objective: To construct a prognostic evaluation model for clear cell renal cell carcinoma (ccRCC) patients using bioinformatics method and to screen potential drugs for ccRCC. Methods: ccRCC RNA sequencing data, clinical data, and protein expression data were downloaded from the TCGA database. Univariate Cox and Lasso regression analyses were performed on the combined data to screen out the proteins related to the prognosis, and they were included in a multivariate Cox proportional hazard model. The patients were divided into high and low-risk groups for a survival difference analysis. The predictive power of the model was evaluated on the basis of overall survival, progression-free survival, independent prognostic, clinically relevant receiver operating characteristic (ROC) curve, C-index, principal component, and clinical data statistics analyses. GSEA enrichment and immune function correlation analyses were performed. The samples were divided into different subtypes based on the expression of the risk proteins, and survival analysis of the subtypes was performed. The risk-related protein and RNA sequencing data were analyzed to screen out sensitive drugs with significant differences between the high and low-risk groups. Results: A total of 469 ccRCC-related proteins were screened, of which 13 proteins with independent prognostic significance were screened by univariate Cox, Lasso, and multivariate Cox regression analyses to construct the prognostic model. The sensitivity and accuracy of the model in predicting the survival of patients with ccRCC were high (1 year: 0.811, 3 years: 0.783, 5 years: 0.777). The 13 proteins were closely related to immunity, and the model proteins were different between kidney and tumor tissues according to the HPA database. The samples were divided into three subtypes, and there were obvious clinical characteristics of the three subtypes in the grade and T, N and M stages. According to the IC50 values, CGP-60474, vinorelbine, doxorubicin, etoposide, FTI-277, JQ12, OSU-03012, pyrimethamine, and other drugs were more sensitive in the high-risk group. Conclusions: A prognostic model of protein expression in ccRCC was successfully constructed, which had good predictive ability for the prognosis of ccRCC patients. The ccRCC-related proteins in the model can be used as targets for studying the pathogenesis and targeted therapy.

Project description:Clear cell renal cell carcinoma (ccRCC) is one of the most aggressive malignancies in humans. Hypoxia-related genes are now recognized as a reflection of poor prognosis in cancer patients with cancer. Meanwhile, immune-related genes play an important role in the occurrence and progression of ccRCC. Nevertheless, reliable prognostic indicators based on hypoxia and immune status have not been well established in ccRCC. The aims of this study were to develop a new gene signature model using bioinformatics and open databases and to validate its prognostic value in ccRCC. The data used for the model structure can be accessed from The Cancer Genome Atlas database. Univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were used to identify the hypoxia- and immune-related genes associated with prognostic risk, which were used to develop a characteristic model of prognostic risk. Kaplan-Meier and receiver-operating characteristic curve analyses were performed as well as independent prognostic factor analyses and correlation analyses of clinical characteristics in both the training and validation cohorts. In addition, differences in tumor immune cell infiltrates were compared between the high and low risk groups. Overall, 30 hypoxia- and immune-related genes were identified, and five hypoxia- and immune-related genes (EPO, PLAUR, TEK, TGFA, TGFB1) were ultimately selected. Survival analysis showed that the high-risk score on the hypoxia- and immune-related gene signature was significantly associated with adverse survival outcomes. Furthermore, clinical ccRCC samples from our medical center were used to validate the differential expression of the five genes in tumor tissue compared to normal tissue through quantitative real-time polymerase chain reaction (qRT-PCR). However, more clinical trials are needed to confirm these results, and future experimental studies must verify the potential mechanism behind the predictive value of the hypoxia- and immune-related gene signature.

Project description:Kidney renal clear cell carcinoma (KIRC) is one of the common malignant tumors of the urinary system. Patients with different risk levels are other in terms of disease progression patterns and disease regression. The poorer prognosis for high-risk patients compared to low-risk patients. Therefore, it is essential to accurately high-risk screen patients and gives accurate and timely treatment. Differential gene analysis, weighted correlation network analysis, Protein-protein interaction network, and univariate Cox analysis were performed sequentially on the train set. Next, the KIRC prognostic model was constructed using the least absolute shrinkage and selection operator (LASSO), and the Cancer Genome Atlas (TCGA) test set and the Gene Expression Omnibus dataset verified the model's validity. Finally, the constructed models were analyzed; including gene set enrichment analysis (GSEA) and immune analysis. The differences in pathways and immune functions between the high-risk and low-risk groups were observed to provide a reference for clinical treatment and diagnosis. A four-step key gene screen resulted in 17 key factors associated with disease prognosis, including 14 genes and 3 clinical features. The LASSO regression algorithm selected the seven most critical key factors to construct the model: age, grade, stage, GDF3, CASR, CLDN10, and COL9A2. In the training set, the accuracy of the model in predicting 1-, 2- and 3-year survival rates was 0.883, 0.819, and 0.830, respectively. The accuracy of the TCGA dataset was 0.831, 0.801, and 0.791, and the accuracy of the GSE29609 dataset was 0.812, 0.809, and 0.851 in the test set. Model scoring divided the sample into a high-risk group and a low-risk group. There were significant differences in disease progression and risk scores between the two groups. GSEA analysis revealed that the enriched pathways in the high-risk group mainly included proteasome and primary immunodeficiency. Immunological analysis showed that CD8 (+) T cells, M1 macrophages, PDCD1, and CTLA4 were upregulated in the high-risk group. In contrast, antigen-presenting cell stimulation and T-cell co-suppression were more active in the high-risk group. This study added clinical characteristics to constructing the KIRC prognostic model to improve prediction accuracy. It provides help to assess the risk of patients more accurately. The differences in pathways and immunity between high and low-risk groups were also analyzed to provide ideas for treating KIRC patients.

Project description:Background: Clear cell renal cell carcinoma (ccRCC) accounts for 80% of all kidney cancers and has a poor prognosis. Recent studies have shown that copper-dependent, regulated cell death differs from previously known death mechanisms (apoptosis, ferroptosis, and necroptosis) and is dependent on mitochondrial respiration (Tsvetkov et al., Science, 2022, 375 (6586), 1254-1261). Studies also suggested that targeting cuproptosis may be a novel therapeutic strategy for cancer therapy. In ccRCC, both cuproptosis and lncRNA were critical, but the mechanisms were not fully understood. The aim of our study was to construct a prognostic profile based on cuproptosis-associated lncRNAs to predict the prognosis of ccRCC and to study the immune profile of clear cell renal cell carcinoma (ccRCC). Methods: We downloaded the transcriptional profile and clinical information of ccRCC from The Cancer Genome Atlas (TCGA). Co-expression network analysis, Cox regression method, and least absolute shrinkage and selection operator (LASSO) method were used to identify cuproptosis-associated lncRNAs and to construct a risk prognostic model. In addition, the predictive performance of the model was validated and recognized by an integrated approach. We then also constructed a nomogram to predict the prognosis of ccRCC patients. Differences in biological function were investigated by GO, KEGG, and immunoassay. Immunotherapy response was measured using tumor mutational burden (TMB) and tumor immune dysfunction and rejection (TIDE) scores. Results: We constructed a panel of 10 cuproptosis-associated lncRNAs (HHLA3, H1-10-AS1, PICSAR, LINC02027, SNHG15, SNHG8, LINC00471, EIF1B-AS1, LINC02154, and MINCR) to construct a prognostic prediction model. The Kaplan-Meier and ROC curves showed that the feature had acceptable predictive validity in the TCGA training, test, and complete groups. The cuproptosis-associated lncRNA model had higher diagnostic efficiency compared to other clinical features. The analysis of Immune cell infiltration and ssGSEA further confirmed that predictive features were significantly associated with the immune status of ccRCC patients. Notably, the superimposed effect of patients in the high-risk group and high TMB resulted in shorter survival. In addition, the higher TIDE scores in the high-risk group suggested a poorer outcome for immune checkpoint blockade response in these patients. Conclusion: The ten cuproptosis-related risk profiles for lncRNA may help assess the prognosis and molecular profile of ccRCC patients and improve treatment options, which can be further applied in the clinic.

Project description:Objective: In order to predict the prognosis in patients with clear cell renal cell carcinoma (ccRCC) so as to understand cancer lipid metabolism and sensitivity to immune-targeting drugs, model algorithms were used to establish a risk coefficient model of long non-coding RNAs (lncRNAs) associated with lipid metabolism. Methods: The transcriptome data were retrieved from TCGA, and lncRNAs associated with lipid metabolism were obtained through Pearson correlation and differential expression analyses. Differentially expressed lipid metabolism-related lncRNAs and lipid metabolism-related lncRNA pairs were obtained using the R language software. The minimum absolute shrinkage method and the selector operation regression method were used to construct the model and draw the receiver operator characteristic curve. High-risk patients were differentiated from low-risk patients through the cut-off value, and the correlation analyses of the high-risk subgroup and low-risk subgroup were performed. Results: This research discovered that 25 pairs of lncRNAs were associated with the lipid metabolism of ccRCC, and 12 of these pairs were utilized to build the model. In combination with clinical data, the areas under the 1-, 3- and 5-year survival curves of ccRCC patients were 0.809, 0.764 and 0.792, separately. The cut-off value was used to perform subgroup analysis. The results showed that high-risk patients had poor prognosis. The results of Cox multivariate regressive analyses revealed that age and risk score were independent prediction factors of ccRCC prognosis. In addition, immune cell infiltration, the levels of gene expression at immune checkpoints, and high-risk patients more susceptible to sunitinib-targeted treatment were assessed by the risk model. Conclusion: Our team identified new prognostic markers of ccRCC and established risk models that could assess the prognosis of ccRCC patients and help determine which type of patients were more susceptible to sunitinib. These discoveries are vital for the optimization of risk stratification and personalized management.

Dataset Information

Construction of an individualized clinical prognostic index based on ubiquitination-associated lncRNA in clear cell renal cell carcinoma patients.

Background

Methods and patients

Results

Conclusion

Publications

Construction of an individualized clinical prognostic index based on ubiquitination-associated lncRNA in clear cell renal cell carcinoma patients.

Similar Datasets

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