Evidence map›Paper›PMID 40596367›Full record

ArticleScientific reports2025

Regulatory T cells and matrix-producing cancer associated fibroblasts contribute on the immune resistance and progression of prognosis related tumor subtypes in ccRCC.

Chao Zhang, Yisu Song, Xiaobo Cui, Yina Wang, Jiang Liu, Zhouji Shen

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In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Chao ZhangDepartment of Nephrology, The affiliated Lihuili Hospital of Ningbo University, Ningbo, 315000, Zhejiang, China.
Yisu SongKey Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province, Hangzhou, 310006, China.
Xiaobo CuiDepartment of Urology, The affiliated Lihuili Hospital of Ningbo University, Ningbo, 315000, Zhejiang, China.
Yina WangDepartment of Nephrology, The affiliated Lihuili Hospital of Ningbo University, Ningbo, 315000, Zhejiang, China.
Jiang LiuDepartment of Nephrology, The affiliated Lihuili Hospital of Ningbo University, Ningbo, 315000, Zhejiang, China. Liujiangzhejiangdb@163.com.
Zhouji ShenDepartment of Nephrology, The affiliated Lihuili Hospital of Ningbo University, Ningbo, 315000, Zhejiang, China. lhlshenzhouji@nbu.edu.cn.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Clear cell renal cell carcinoma (ccRCC) is a prevalent malignant tumor in the field of urology. The effect of cell heterogeneity on the prognosis and reaction to treatment of ccRCC in large populations is still unclear. By analyzing public single cell RNA-sequencing and bulk RNA-sequencing data with the Scissor algorithm, we have identified three distinct prognosis related cancer cell subtypes which play an indispensable role on tumor metastasis, immune response and proliferation respectively. Besides, regulatory T cells (Tregs) and matrix producing cancer associated fibroblasts (matCAFs) were also recognized as crucial cell subtypes in the tumor microenvironment (TME). Moreover, potential interactions between Scissor + cells and other cells in TME were investigated to uncover regulatory mechanisms via 'Cell Chat' and cell2location algorithm. It is interesting that the interferon gamma signaling pathway and p53 signaling pathway contribute to the Scissor + transition of Tregs and matCAFs. The distinct activated transcription factor patterns were uncovered as well as the essential ligand-receptor pairs in the interactions among different cell subtypes, such as CXCL12-CXCR4 and COL6A2-SDC4. Then, we developed a risk score signature consisting of 10 genes, utilizing a 101-combination machine learning computational framework, which showed promising results in predicting the prognosis of patients. Furthermore, our study revealed variations in immune cell infiltration and the expression of immune related factors within the tumor microenvironment between different risk score groups, as well as the different sensitivity to the immunotherapy. In the end, we suggested Rapamycin as the additional therapy for the advanced ccRCC. In conclusion, our study created a signature to provide opportunities for predicting prognosis and improving treatments of ccRCC.

Indexed as

Cancer-Associated FibroblastsCarcinoma, Renal CellKidney NeoplasmsT-Lymphocytes, RegulatoryDisease ProgressionGene Expression Regulation, NeoplasticHumansPrognosisSignal TransductionTumor MicroenvironmentClear cell renal carcinomaMachine learningMulti-omicsScissor

Identifiers

PMID40596367
PMCPMC12214632

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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.