Dataset Information

Survival prediction of kidney renal papillary cell carcinoma by comprehensive LncRNA characterization.

ABSTRACT: Kidney renal papillary cell carcinoma (KIRP) accounts for 10%-15% of renal cell carcinoma (RCC), patients with KIRP tend to have a poor prognosis, and there was a lack of effective prognostic indicators for this type of cancer. Currently, owing to the availability of The Cancer Genome Atlas (TCGA), long non-coding RNAs (LncRNAs) have been discovered to indicate a prognostic value in some tumors. In that regard, we analyzed lncRNA-sequencing data of KIRP in TCGA, and among 780 differentially-expressed lncRNAs, we selected 37 lncRNAs which were able to assist the prognosis. In addition, by using the multivariate cox regression analysis, the prognosis index (PI) that consisted of 7 lncRNAs (including AFAP1-AS1, GAS6-AS1, RP11-1C8.7, RP11-21L19.1, RP11-503C24.1, RP11-536I6.2, and RP11-63A11.1) could predict the progression and outcomes of KIRP with accuracy. More importantly, the PI was considered an independent indicator for prognostication of KIRP. Moreover, having categorized patients with KIRP into cohorts of high risk and low risk, according to the PI, we found that the key genes and pathways varied in these two groups. Overall, these LncRNAs, especially the PI, may be conceived as biomarkers and helpful for determining the different pathological stages for KIRP patients. However, their biological functions need to be further confirmed.

SUBMITTER: Lan H

PROVIDER: S-EPMC5762286 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Survival prediction of kidney renal papillary cell carcinoma by comprehensive LncRNA characterization.

Lan Huihua H Zeng Jianghui J Chen Gang G Huang Huayi H

Oncotarget 20171128 67

Kidney renal papillary cell carcinoma (KIRP) accounts for 10%-15% of renal cell carcinoma (RCC), patients with KIRP tend to have a poor prognosis, and there was a lack of effective prognostic indicators for this type of cancer. Currently, owing to the availability of The Cancer Genome Atlas (TCGA), long non-coding RNAs (LncRNAs) have been discovered to indicate a prognostic value in some tumors. In that regard, we analyzed lncRNA-sequencing data of KIRP in TCGA, and among 780 differentially-expr ...[more]

PMID: 29340018

Similar Datasets

Project description:BackgroundClear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer whose incidence and mortality rate are increasing. Identifying immune-related lncRNAs and constructing a model would probably provide new insights into biomarkers and immunotherapy for ccRCC and aid in the prognosis prediction.MethodsThe transcription profile and clinical information were obtained from The Cancer Genome Atlas (TCGA). Immune-related gene sets and transcription factor genes were downloaded from GSEA website and Cistrome database, respectively. Tumor samples were divided into the training set and the testing set. Immune-related differentially expressed lncRNAs (IDElncRNAs) were identified from the whole set. Univariate Cox regression, LASSO, and stepwise multivariate Cox regression were performed to screen out ideal prognostic IDElncRNAs (PIDElncRNAs) from the training set and develop a multi-lncRNA signature.ResultsConsequently, AC012236.1, AC078778.1, AC078950.1, AC087318.1, and AC092535.4 were screened to be significantly related to the prognosis of ccRCC patients, which were used to establish the five-lncRNA signature. Its wide diagnostic capacity was revealed in different subgroups of clinical parameters. Then AJCC-stage, Fuhrman-grade, pharmaceutical, age, and risk score regarded as independent prognostic factors were integrated to construct a nomogram, whose good performance in predicting 3-, 5-, and 7-year overall survival of ccRCC patients was revealed by time-dependent ROC curves and verified by the testing sets and ICGC dataset. The calibration plots showed great agreement of the nomogram between predicted and observed outcomes. Functional enrichment analysis showed the signature and each lncRNA were mainly enriched in pathways associated with regulation of immune response. Several kinds of tumor-infiltrating immune cells like regulatory T cells, T follicular helper cells, CD8+ T cells, resting mast cells, and naïve B cells were significantly correlated with the signature.ConclusionTherefore, we constructed a five-lncRNA model integrating clinical parameters to help predict the prognosis of ccRCC patients. The five immune-related lncRNAs could potentially be therapeutic targets for immunotherapy in ccRCC in the future.

Dataset Information

Survival prediction of kidney renal papillary cell carcinoma by comprehensive LncRNA characterization.

Publications

Survival prediction of kidney renal papillary cell carcinoma by comprehensive LncRNA characterization.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure