Evidence map›Paper›PMID 37558766›Full record

ArticleJournal of cancer research and clinical oncology2023

A novel coiled-coil domain containing-related gene signature for predicting prognosis and treatment effect of breast cancer.

Yufei Wang, Yanmei Wang, Jia Zhou, Pingting Ying, Zhuo Wang, Yan Wu, Minyan Hao, Shuying Qiu, Hongchuan Jin, Xian Wang

Abstract read
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In one paragraph

Article in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. Article
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.

Yufei Wang *Department of Medical Oncology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
Yanmei Wang *Department of Medical Oncology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
Jia ZhouDepartment of Medical Oncology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China. zhoujia90@zju.edu.cn.
Pingting YingLaboratory of Cancer Biology, Key Lab of Biotherapy in Zhejiang Province, Cancer Center of Zhejiang University, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
Zhuo WangDepartment of Medical Oncology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
Yan WuLaboratory of Cancer Biology, Key Lab of Biotherapy in Zhejiang Province, Cancer Center of Zhejiang University, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
Minyan HaoDepartment of Medical Oncology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
Shuying QiuLaboratory of Cancer Biology, Key Lab of Biotherapy in Zhejiang Province, Cancer Center of Zhejiang University, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
Hongchuan JinLaboratory of Cancer Biology, Key Lab of Biotherapy in Zhejiang Province, Cancer Center of Zhejiang University, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
Xian WangDepartment of Medical Oncology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China. wangx118@zju.edu.cn.

Funding

National Natural Science Foundation of China No. 32000519Natural Science Foundation of Zhejiang Province No. LQ21C080001
6 · The paper itself

Abstract

purposeBreast cancer (BRCA) is a prevalent tumor worldwide. The association between the coiled-coil domain-containing (CCDC) protein family and different tumors has been established. However, the prognostic significance of this protein family in breast cancer remains uncertain.

methodsGene expression and clinical data were obtained from the TCGA, METABRIC, and GEO databases. Prognosis genes were identified using univariate Cox and LASSO Cox regression, leading to the establishment of a prognostic signature. Subsequently, the risk model was conducted based on survival and clinical feature analyses, and a nomogram for prognosis prediction was developed. Furthermore, analyses of biological function, immune characteristics, and drug sensitivity were performed. Finally, single-cell sequencing data were utilized to uncover the expression patterns of genes in the risk model.

resultsFive genes were identified and utilized for risk modeling. The model demonstrated excellent prognostic value as indicated by ROC and Kaplan-Meier analysis. The high-risk group exhibited shorter survival time and higher likelihood of recurrence. Functional annotation indicated a correlation between the risk score and immune pathways. Conversely, the low-risk group displayed a greater enrichment in immune pathways and exhibited more active immune microenvironment characteristics. Additionally, drug sensitivity analysis using both public and our sequencing data revealed that the risk model possessed a broad range of predictive values.

conclusionsWe have developed a gene signature and have verified that patients with low-risk are more likely to have better prognosis and respond positively to therapy. This finding offers a valuable point of reference for BRCA individualized treatment.

Indexed as

Breast cancer (BRCA)Coiled-coil domain containing (CCDC)Drug sensitivityImmune cell infiltration

Identifiers

PMID37558766
PMCPMC11797672

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