Evidence map›Paper›PMID 34712244›Full record

ArticleFrontiers in immunology2021

Immunogenomic Analyses of the Prognostic Predictive Model for Patients With Renal Cancer.

Tao Feng, Jiahui Zhao, Dechao Wei, Pengju Guo, Xiaobing Yang, Qiankun Li, Zhou Fang, Ziheng Wei, Mingchuan Li, Yongguang Jiang and 1 more

Abstract read
In one paragraph

Article in Frontiers in immunology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 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

11 authors.

Tao FengDepartment of Urology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
Jiahui ZhaoDepartment of Urology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
Dechao WeiDepartment of Urology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
Pengju GuoDepartment of Urology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
Xiaobing YangDepartment of Urology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
Qiankun LiDepartment of Urology, Beijing Huairou Hospital, Beijing, China.
Zhou FangDepartment of Cardiovascular Surgery, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
Ziheng WeiDepartment of Cardiovascular Surgery, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
Mingchuan LiDepartment of Urology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
Yongguang JiangDepartment of Urology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
Yong LuoDepartment of Urology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Renal cell carcinoma (RCC) is associated with poor prognostic outcomes. The current stratifying system does not predict prognostic outcomes and therapeutic benefits precisely for RCC patients. Here, we aim to construct an immune prognostic predictive model to assist clinician to predict RCC prognosis. Methods: Herein, an immune prognostic signature was developed, and its predictive ability was confirmed in the kidney renal clear cell carcinoma (KIRC) cohorts based on The Cancer Genome Atlas (TCGA) dataset. Several immunogenomic analyses were conducted to investigate the correlations between immune risk scores and immune cell infiltrations, immune checkpoints, cancer genotypes, tumor mutational burden, and responses to chemotherapy and immunotherapy. Results: The immune prognostic signature contained 14 immune-associated genes and was found to be an independent prognostic factor for KIRC. Furthermore, the immune risk score was established as a novel marker for predicting the overall survival outcomes for RCC. The risk score was correlated with some significant immunophenotypic factors, including T cell infiltration, antitumor immunity, antitumor response, oncogenic pathways, and immunotherapeutic and chemotherapeutic response. Conclusions: The immune prognostic, predictive model can be effectively and efficiently used in the prediction of survival outcomes and immunotherapeutic responses of RCC patients.

Indexed as

AgedFemaleGenomicsHumansImmunotherapyKaplan-Meier EstimateKidney NeoplasmsMaleMolecular Docking SimulationMutationPrognosisRisk FactorsT-LymphocytesTranscriptomeimmunotherapyprognostic modelrenal cell carcinomarisk scoretumor immune microenvironment

Identifiers

PMID34712244
PMCPMC8546215

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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.