Evidence map›Paper›PMID 39458936›Full record

ArticlePharmaceuticals (Basel, Switzerland)2024

Identification of Key Immune and Cell Cycle Modules and Prognostic Genes for Glioma Patients through Transcriptome Analysis.

Kaimin Guo, Jinna Yang, Ruonan Jiang, Xiaxia Ren, Peng Liu, Wenjia Wang, Shuiping Zhou, Xiaoguang Wang, Li Ma, Yunhui Hu

Abstract read
In one paragraph

Article in Pharmaceuticals (Basel, Switzerland), 2024. 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

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

2 citing papers in PubMed.

  1. Review
  2. 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.

Kaimin GuoTianjin Tasly Digital Intelligence Chinese Medicine Development Co., Ltd., Tianjin 300410, China.ORCID 0000-0001-8069-7300
Jinna YangTianjin Tasly Digital Intelligence Chinese Medicine Development Co., Ltd., Tianjin 300410, China.
Ruonan JiangDepartment of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin 300052, China.
Xiaxia RenTianjin Tasly Digital Intelligence Chinese Medicine Development Co., Ltd., Tianjin 300410, China.
Peng LiuState Key Laboratory of Chinese Medicine Modernization, Tianjin 300193, China.
Wenjia WangTianjin Tasly Digital Intelligence Chinese Medicine Development Co., Ltd., Tianjin 300410, China.
Shuiping ZhouTianjin Tasly Digital Intelligence Chinese Medicine Development Co., Ltd., Tianjin 300410, China.
Xiaoguang WangTianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China.
Li MaTianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China.
Yunhui HuTianjin Tasly Digital Intelligence Chinese Medicine Development Co., Ltd., Tianjin 300410, China.

Funding

National Natural Science Foundation of China 82272639Tianjin Natural Science Foundation 22JCYBJC00180
6 · The paper itself

Abstract

backgroundGliomas, the most prevalent type of primary brain tumor, stand out as one of the most aggressive and lethal types of human cancer. METHODS &

resultsTo uncover potential prognostic markers, we employed the weighted correlation network analysis (WGCNA) on the Chinese Glioma Genome Atlas (CGGA) 693 dataset to reveal four modules significantly associated with glioma clinical traits, primarily involved in immune function, cell cycle regulation, and ribosome biogenesis. Using the least absolute shrinkage and selection operator (LASSO) regression algorithm, we identified 11 key genes and developed a prognostic risk score model, which exhibits precise prognostic prediction in the CGGA 325 dataset. More importantly, we also validated the model in 12 glioma patients with overall survival (OS) ranging from 4 to 132 months using mRNA sequencing and immunohistochemical analysis. The analysis of immune infiltration revealed that patients with high-risk scores exhibit a heightened immune infiltration, particularly immune suppression cells, along with increased expression of immune checkpoints. Furthermore, we explored potentially effective drugs targeting 11 key genes for gliomas using the library of integrated network-based cellular signatures (LINCS) L1000 database, identifying that in vitro, both torin-1 and clofarabine exhibit promising anti-glioma activity and inhibitory effect on the cell cycle, a significant pathway enriched in the identified glioma modules.

conclusionsIn conclusion, our study provides valuable insights into molecular mechanisms and identifying potential therapeutic targets for gliomas.

Indexed as

drug screeninggliomaLASSOprognostic signaturetranscriptometumor microenvironmentWGCNA

Identifiers

PMID39458936
PMCPMC11514598

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