Dataset Information

Development of an Autophagy Score Signature for Predicting Overall Survival in Papillary Renal Cell Carcinoma.

ABSTRACT: Background: Autophagy is considered to be closely associated with cancer, functioning as either an anticancer or procancer mechanism depending on the cancer stage. However, the prognostic value of autophagy on papillary renal cell carcinoma (pRCC) remains unclear. In this study, our purpose was to determine the autophagy-related mRNA signature to predict the overall survival of patients with pRCC.

Materials and methods: A total of 284 patients with pathologic confirmed pRCC in The Cancer Genome Atlas (TCGA) dataset were recruited and included. We choose patients who have smoked less than 15 years but staging 3 or 4 (including nontobacco exposure) vs. more than 15 years but staging 1 or 2. Fourteen differentially expressed mRNAs were found with fold?change > 2 and P value < 0.001 through limma package after making a pair between nontobacco exposure or less than 15 years and tobacco exposure more than 15 years by matchIt package.

Results: Six mRNAs were identified to be significantly associated with overall survival. Then, using a risk score based on the signature of these six mRNAs, we divided the patients into low-risk and high-risk groups with significantly different OS. Further multivariate Cox regression analyses revealed that the 6-mRNA signature was independent of age, TNM stage, and tumor type. In the present study, a novel 6-mRNA signature that is useful in survival prediction in pRCC patients was developed. If validated, this mRNA signature might assist in selecting high-risk subpopulation that needs more aggressive therapeutic intervention. The risk score involved in several cancer-related pathways was identified using gene set enrichment analysis.

Conclusion: We initially generated a six autophagy-related genes' signature, which correlates with AJCC N stage, tumor type, and pathological stage and independently predicts OS.

SUBMITTER: Gu X

PROVIDER: S-EPMC7684156 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Development of an Autophagy Score Signature for Predicting Overall Survival in Papillary Renal Cell Carcinoma.

Gu Xiang X Chen Xiaojun X Zhu Lei L Song Wenbo W Wang Ali A Chu Junfeng J Wang Tao T Jiang Peng P Ge Yizhi Y

Disease markers 20201109

<h4>Background</h4>Autophagy is considered to be closely associated with cancer, functioning as either an anticancer or procancer mechanism depending on the cancer stage. However, the prognostic value of autophagy on papillary renal cell carcinoma (pRCC) remains unclear. In this study, our purpose was to determine the autophagy-related mRNA signature to predict the overall survival of patients with pRCC.<h4>Materials and methods</h4>A total of 284 patients with pathologic confirmed pRCC in The C ...[more]

PMID: 33273989

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Project description:BackgroundHepatocellular carcinoma (HCC) remains a major challenge for public health worldwide. Considering the great heterogeneity of HCC, more accurate prognostic models are urgently needed. To identify a robust prognostic gene signature, we conduct this study.Materials and methodsLevel 3 mRNA expression profiles and clinicopathological data were obtained in The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC). GSE14520 dataset from the gene expression omnibus (GEO) database was downloaded to further validate the results in TCGA. Differentially expressed mRNAs between HCC and normal tissue were investigated. Univariate Cox regression analysis and lasso Cox regression model were performed to identify and construct the prognostic gene signature. Time-dependent receiver operating characteristic (ROC), Kaplan-Meier curve, multivariate Cox regression analysis, nomogram, and decision curve analysis (DCA) were used to assess the prognostic capacity of the six-gene signature. The prognostic value of the gene signature was further validated in independent GSE14520 cohort. Gene Set Enrichment Analyses (GSEA) was performed to further understand the underlying molecular mechanisms. The performance of the prognostic signature in differentiating between normal liver tissues and HCC were also investigated.ResultsA novel six-gene signature (including CSE1L, CSTB, MTHFR, DAGLA, MMP10, and GYS2) was established for HCC prognosis prediction. The ROC curve showed good performance in survival prediction in both the TCGA HCC cohort and the GSE14520 validation cohort. The six-gene signature could stratify patients into a high- and low-risk group which had significantly different survival. Cox regression analysis showed that the six-gene signature could independently predict OS. Nomogram including the six-gene signature was established and shown some clinical net benefit. Furthermore, GSEA revealed several significantly enriched oncological signatures and various metabolic process, which might help explain the underlying molecular mechanisms. Besides, the prognostic signature showed a strong ability for differentiating HCC from normal tissues.ConclusionsOur study established a novel six-gene signature and nomogram to predict overall survival of HCC, which may help in clinical decision making for individual treatment.

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Dataset Information

Development of an Autophagy Score Signature for Predicting Overall Survival in Papillary Renal Cell Carcinoma.

Publications

Development of an Autophagy Score Signature for Predicting Overall Survival in Papillary Renal Cell Carcinoma.

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