Evidence map›Paper›PMID 36091022›Full record

ArticleFrontiers in immunology2022

An immunotherapy response prediction model derived from proliferative CD4

Kun Zheng, Lianchong Gao, Jie Hao, Xin Zou, Xiaoyong Hu

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
2.4field-weighted citation impact, top 9% of its field
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

14 citing papers in PubMed, 22 citations in OpenAlex.

  1. Predictive model for clear cell renal cell carcinoma: a novel model integrating sunitinib resistance and prognosis related genes and clinical factors.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
    Article
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  4. Role of CD4Journal of translational medicine · 2025
    Review
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  9. Immunotherapy and Cancer: The Multi-Omics Perspective.International journal of molecular sciences · 2024
    Review
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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 at 5 institutions in 1 country.

Kun ZhengDepartment of Urology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.
Lianchong GaoYantai Institute, China Agricultural University, Yantai, China.
Jie HaoInstitute of Clinical Science, Zhongshan Hospital, Fudan University, Shanghai, China.
Xin ZouCenter for Tumor Diagnosis & Therapy, Jinshan Hospital, Fudan University, Shanghai, China.
Xiaoyong HuDepartment of Urology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.
China Agricultural University · CNJinshan Hospital of Fudan University · CNShanghai Jiao Tong University · CNShanghai Sixth People's Hospital · CNSun Yat-sen University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Most patients with clear cell renal cell carcinoma (ccRCC) have an impaired response to immune checkpoint blockade (ICB) therapy. Few biomarkers can predict responsiveness, and there is insufficient evidence to extend them to ccRCC clinical use. To explore subtypes and signatures of immunocytes with good predictive performance for ICB outcomes in the ccRCC context, we reanalyzed two ccRCC single-cell RNA sequencing (scRNA-seq) datasets from patients receiving ICB treatment. A subtype of proliferative CD4

Indexed as

Carcinoma, Renal CellKidney NeoplasmsCD4-Positive T-LymphocytesHumansImmunotherapyMonocytesclear cell renal cell carcinoma (ccRCC)immune checkpoint blockade (ICB) therapymachine learning (ML)response prediction modelsingle-cell RNA-seq

Identifiers

PMID36091022
PMCPMC9452905
OpenAlexW4293018148

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.