Evidence map›Paper›PMID 39796714›Full record

ArticleCancers2024

Single-Cell and Bulk Transcriptomics Reveal the Immunosenescence Signature for Prognosis and Immunotherapy in Lung Cancer.

Yakun Zhang, Jiajun Zhou, Yitong Jin, Chenyu Liu, Hanxiao Zhou, Yue Sun, Han Jiang, Jing Gan, Caiyu Zhang, Qianyi Lu and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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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

14 authors.

Yakun ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Jiajun ZhouCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Yitong JinThe Second Affiliated Hospital of Harbin Medical University, Harbin 150081, China.
Chenyu LiuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Hanxiao ZhouCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Yue SunCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Han JiangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Jing GanCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Caiyu ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Qianyi LuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Yetong ChangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Yunpeng ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Xia LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Shangwei NingCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.ORCID 0000-0003-4079-8945

Funding

China Brain Project 2021ZD0202403National Natural Science Foundation of China 32070672, 32370718, 32070673, 62172131, U23A20166Outstanding Youth Foundation of Heilongjiang Province of China YQ2022C034, YQ2021C026
6 · The paper itself

Abstract

backgroundImmunosenescence is the aging of the immune system, which is closely related to the development and prognosis of lung cancer. Targeting immunosenescence is considered a promising therapeutic approach.

methodsWe defined an immunosenescence gene set (ISGS) and examined it across 33 TCGA tumor types and 29 GTEx normal tissues. We explored the 46,993 single cells of two lung cancer datasets. The immunosenescence risk model (ISRM) was constructed in TCGA LUAD by network analysis, immune infiltration analysis, and lasso regression and validated by survival analysis, cox regression, and nomogram in four lung cancer cohorts. The predictive ability of ISRM for drug response and immunotherapy was detected by the oncopredict algorithm and XGBoost model.

resultsWe found that senescent lung tissues were significantly enriched in ISGS and revealed the heterogeneity of immunosenescence in pan-cancer. Single-cell and bulk transcriptomics characterized the distinct immune microenvironment between old and young lung cancer. The ISGS network revealed the crucial function modules and transcription factors. Multiplatform analysis revealed specific associations between immunosenescence and the tumor progression of lung cancer. The ISRM consisted of five risk genes (CD40LG, IL7, CX3CR1, TLR3, and TLR2), which improved the prognostic stratification of lung cancer across multiple datasets. The ISRM showed robustness in immunotherapy and anti-tumor therapy. We found that lung cancer patients with a high-risk score showed worse survival and lower expression of immune checkpoints, which were resistant to immunotherapy.

conclusionsOur study performed a comprehensive framework for assessing immunosenescence levels and provided insights into the role of immunosenescence in cancer prognosis and biomarker discovery.

Indexed as

immunosenescenceimmunotherapylung cancerprognosistranscriptomics

Identifiers

PMID39796714
PMCPMC11720133

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