Evidence map›Paper›PMID 34187413›Full record

ArticleBMC cancer2021

Identification of an independent immune-genes prognostic index for renal cell carcinoma.

Guangyao Li, Xiyi Wei, Shifeng Su, Shangqian Wang, Wei Wang, Yichun Wang, Xianghu Meng, Jiadong Xia, Ninghong Song, Chao Qin

Abstract read
In one paragraph

Article in BMC cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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

10 authors.

Guangyao Li *Department of Urology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
Xiyi Wei *Department of Urology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
Shifeng Su *Department of Urology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
Shangqian WangDepartment of Urology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
Wei WangDepartment of Urology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
Yichun WangDepartment of Urology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
Xianghu MengDepartment of Urology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
Jiadong XiaDepartment of Urology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
Ninghong SongDepartment of Urology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China. songninghong@126.com.
Chao QinDepartment of Urology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China. qinchao@njmu.edu.cn.

Funding

National Natural Science Foundation of China 81672531National Natural Science Foundation of China 81972386Six Talent Peaks Project in Jiangsu Province WSN-011
6 · The paper itself

Abstract

backgroundConsiderable evidence has indicated an association between the immune microenvironment and clinical outcome in ccRCC. The purpose of this study is to extensively figure out the influence of immune-related genes of tumors on the prognosis of patients with ccRCC.

methodsFiles containing 2498 immune-related genes were obtained from the Immunology Database and Analysis Portal (ImmPort), and the transcriptome data and clinical information relevant to patients with ccRCC were identified and downloaded from the TCGA data-base. Univariate and multivariate Cox regression analyses were used to screen out prognostic immune genes. The immune risk score model was established in light of the regression coefficient between survival and hub immune-related genes. We eventually set up a nomogram for the prediction of the overall survival for ccRCC. Kaplan-Meier (K-M) and ROC curve was used in evaluating the value of the predictive risk model. A P value of < 0.05 indicated statistically significant differences throughout data analysis.

resultsVia differential analysis, we found that 556 immune-related genes were expressed differentially between tumor and normal tissues (p < 0. 05). The analysis of univariate Cox regression exhibited that there was a statistical correlation between 43 immune genes and survival risk in patients with ccRCC (p < 0.05). Through Lasso-Cox regression analysis, we established an immune genetic risk scoring model based on 18 immune-related genes. The high-risk group showed a bad prognosis in K-M analysis. (p < 0.001). ROC curve showed that it was reliable of the immune risk score model to predict survival risk (5 year over survival, AUC = 0.802). The model indicated satisfactory AUC and survival correlation in the validation data set (5 year OS, Area Under Curve = 0.705, p < 0.05). From Multivariate regression analysis, the immune-risk score model plays an isolated role in the prediction of the prognosis of ccRCC. Under multivariate-Cox regression analysis, we set up a nomogram for comprehensive prediction of ccRCC patients' survival rate. At last, it was identified that 18 immune-related genes and risk scores were not only tremendously related to clinical prognosis but also contained in a variety of carcinogenic pathways.

conclusionIn general, tumor immune-related genes play essential roles in ccRCC development and progression. Our research established an unequal 18-immune gene risk index to predict the prognosis of ccRCC visually. This index was found to be an independent predictive factor for ccRCC.

Indexed as

AdultAgedAged, 80 and overCarcinoma, Renal CellFemaleGene Expression ProfilingHumansKidney NeoplasmsMaleMiddle AgedPrognosisClear cell renal cell carcinomaImmune-related genesImmune risk score modelNomogramPrognosis

Identifiers

PMID34187413
PMCPMC8240194

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

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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 Socratic graph.