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An epithelial-mesenchymal transition-related long non-coding RNA signature to predict overall survival and immune microenvironment in kidney renal clear cell carcinoma.

ABSTRACT: Kidney renal clear cell carcinoma (ccRCC) is a malignant tumor originating from renal tubular epithelium, lncRNAs can regulate the occurrence and development of EMT by targeting EMT transcription factors. We constructed a new survival signature based on EMT-related differentially expressed lncRNAs obtained from the Cancer Genome Atlas (TCGA-KIRC). We first determined 1377 EMT-related lncRNAs, lncRNA AL035661.1 with the largest correlation coefficient and the target gene was PFN2 (cor = 0.843; P= 1.37E-146). Meanwhile, we found an AUC of 0.758 in our signature and we predicted the AUC values of the patients' 1, 2, 3-year survival rate as 0.768, 0.749, and 0.762 in TCGA cohort, respectively. Multivariate COX analysis was performed to determine if risk score was an independent prognostic predictor of OS. The results indicated that our risk score can be an independent predictor for OS (Univariate: HR = 1.350, 95% CI = 1.276-1.428, P< 0.001; Multivariate: HR = 1.295, 95% CI = 1.201-1.396, P< 0.001). We identified novel EMT-related lncRNAs markers for ccRCC prognosis. The underlying mechanism between EMT-related lncRNAs in ccRCC and tumor immunity is still unclear and requires further study.

SUBMITTER: Zhang YP

PROVIDER: S-EPMC8806254 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundA rising amount of data demonstrates that the epithelial-mesenchymal transition (EMT) in clear cell renal cell carcinomas (ccRCC) is connected with the advancement of the cancer. In order to understand the role of EMT in ccRCC, it is critical to integrate molecules involved in EMT into prognosis prediction. The objective of this project was to establish a prognosis prediction model using genes associated with EMT in ccRCC.MethodsWe acquired the mRNA expression profiles and clinical information about ccRCC from TCGA database. In this study, we measured differentially expressed EMT-related genes (DEEGs) by two comparison groups (tumor versus normal tissues; "stages I-II" versus "stages III-IV" tumor tissues). Based on classification and regression random forest models, we identified the most important DEEGs in predicting prognosis. Afterwards, a risk-score model was created using the identified important DEEGs. The prediction ability of the risk-score model was calculated by the area under the curve (AUC). A nomogram for prognosis prediction was built using the risk-score in combination with clinical factors.ResultsAmong the 72 DEEGs, the classification and regression random forest models identified six hub genes (DKK1, DLX4, IL6, KCNN4, RPL22L1, and SPDEF), which exhibited the highest importance values in both models. Through the expression of these six hub genes, a novel risk-score was developed for the prognosis prediction of ccRCC. ROC curves showed the risk-score performed well in both the training (0.749) and testing (0.777) datasets. According to the survival analysis, individuals who were separated into high/low-risk groups had statistically different outcomes in terms of prognosis. Besides, the risk-score model also showed outstanding ability in assessing the progression of ccRCC after treatment. In terms of nomogram, the concordance index (C-index) was 0.79. Additionally, we predicted the differences in response to chemotherapy drugs among patients from low- and high-risk groups.ConclusionGene signatures related to EMT could be useful in predicting ccRCC prognosis.

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An epithelial-mesenchymal transition-related long non-coding RNA signature to predict overall survival and immune microenvironment in kidney renal clear cell carcinoma.

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