Evidence map›Paper›PMID 32883362›Full record

ArticleHereditas2020

A seven-gene signature model predicts overall survival in kidney renal clear cell carcinoma.

Ling Chen, Zijin Xiang, Xueru Chen, Xiuting Zhu, Xiangdong Peng

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Hereditas, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Ling ChenDepartment of Gastrointestinal Surgery, Xiangya Hospital, Central South University, Changsha, 410013, Hunan, China.
Zijin XiangDepartment of Pharmacy, The Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan, China.
Xueru ChenDepartment of Pharmacy, The Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan, China.
Xiuting ZhuDepartment of Pharmacy, The Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan, China.
Xiangdong PengDepartment of Pharmacy, The Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan, China. xdpeng@csu.edu.cn.ORCID http://orcid.org/0000-0002-6744-8809

Funding

Hunan Provincial Natural Science Foundation of China 2018JJ2600Key research and development plan of hunan province 2018sk2129Project of Scientific Research Plan of Hunan Provincial Health Commission B2017029
6 · The paper itself

Abstract

backgroundKidney renal clear cell carcinoma (KIRC) is a potentially fatal urogenital disease. It is a major cause of renal cell carcinoma and is often associated with late diagnosis and poor treatment outcomes. More evidence is emerging that genetic models can be used to predict the prognosis of KIRC. This study aimed to develop a model for predicting the overall survival of KIRC patients.

resultsWe identified 333 differentially expressed genes (DEGs) between KIRC and normal tissues from the Gene Expression Omnibus (GEO) database. We randomly divided 591 cases from The Cancer Genome Atlas (TCGA) into training and internal testing sets. In the training set, we used univariate Cox regression analysis to retrieve the survival-related DEGs and futher used multivariate Cox regression with the LASSO penalty to identify potential prognostic genes. A seven-gene signature was identified that included APOLD1, C9orf66, G6PC, PPP1R1A, CNN1G, TIMP1, and TUBB2B. The seven-gene signature was evaluated in the training set, internal testing set, and external validation using data from the ICGC database. The Kaplan-Meier analysis showed that the high risk group had a significantly shorter overall survival time than the low risk group in the training, testing, and ICGC datasets. ROC analysis showed that the model had a high performance with an AUC of 0.738 in the training set, 0.706 in the internal testing set, and 0.656 in the ICGC external validation set.

conclusionOur findings show that a seven-gene signature can serve as an independent biomarker for predicting prognosis in KIRC patients.

Indexed as

Biomarkers, TumorTranscriptomeArea Under CurveCarcinoma, Renal CellComputational BiologyDatabases, GeneticGene Expression ProfilingGene OntologyHumansKidney NeoplasmsPrognosisROC CurveBiomarkers, TumorBioinformaticsKidney renal clear cell carcinomaLASSO penaltyPrognostic model

Identifiers

PMID32883362
PMCPMC7470605

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.